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 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 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 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 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 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 show_cts(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_cts(filename, 10)
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 = []
references = []
name = _record_name(filename)
for i, item in enumerate(record.signals):
if item is None:
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)
references += _plot_references(name, onset, offset)
legend_items = {
_LABEL_MEASURES: measurements[0],
_LABEL_POINTS: local_points[0]
}
if references:
legend_items[_LABEL_REFERENCES] = references[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
elif label == _LABEL_REFERENCES:
lines = references
for item in lines:
item.set_visible(not item.get_visible())
plt.draw()
triggers.on_clicked(switch)
_result_table(measures, name, ((10, 8), (10, 8), (6, 5), (12, 10)),
"durations.pkl")
plt.get_current_fig_manager().window.state("zoomed")
plt.gcf().canvas.set_window_title("CTS-ECG: " + name)
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 _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 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()
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()
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 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
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()
def draw_checkboxes():
global check_if, check_stat
if check_if == None:
rax = plt.axes([0.01, 0.8, 0.05, 0.05], title="interfaces")
check_if = CheckButtons(rax, interface_list.keys(),
interface_list.values())
check_if.on_clicked(partial(oncheck))
if check_stat == None:
rax = plt.axes([0.01, 0.5, 0.08, 0.15], title="stats")
vis = [value[1] for value in stats.values()]
check_stat = CheckButtons(rax, stats.keys(), vis)
check_stat.on_clicked(partial(oncheck_stats))
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 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()
class choose_curve (event_handler) :
"""
creates button to decide which curves will be visible
"""
def __init__ ( self , curves , name_curves = None , recenter = True , ax = None , fig = None) :
"""
curves: list of plot.
name_curves : name_curves[i] is the name of the curve curves[i]. Must the same length as curves.
recenter : recenter the graph each time we add or remove a plot.
"""
super().__init__(ax,fig)
self.curves = curves
self.name_curves = ['curve %d'%(i+1) for i in range(len(data))] \
if name_curves is None else name_curves
self.recenter = recenter
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)
#not usual activate because of the button so overwrite activate and activate_on_ax
def activate ( self ) :
self.add_button(self.ax)
def activate_on_ax (self , ax) :
self.add_button(ax)
def on_click(self,label):
#get the current curve
curve = self.curves[self.name_curves.index(label)]
#set it invisivle if it is visible and vice versa
curve.set_visible(not curve.get_visible())
if self.recenter :
# recompute the ax.dataLim
self.ax.relim(visible_only=True)
# update ax using the new dataLim
self.ax.autoscale()
#redraw graph
plt.draw()
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()
class Controller:
'''
Display coordinate of the point if mouse hover on the plot
'''
@staticmethod
def decimal_quantize(value, format='.00', rounding=ROUND_HALF_UP):
return float(Decimal(value).quantize(Decimal('.00'), ROUND_HALF_UP))
def __init__(self, fig, ax):
self.__fig = fig
self.__ax = ax
self.__point_annot = self.__ax.annotate("", (0, 0), xytext=(5, 5),textcoords='offset points')
self.__cursor = Cursor(ax, useblit=False, color='red', linewidth=0.5)
self.__checkbox_ax = self.__fig.add_axes([0.02, 0.65, 0.1, 0.15], facecolor="lightgoldenrodyellow")
self.__checkbox = CheckButtons(self.__checkbox_ax, ["curve\nfitting"], [self.__ax.get_visible()])
self.__slider_ax = self.__fig.add_axes([0.3, 0.02, 0.5, 0.02], facecolor="lightgoldenrodyellow")
self.__slider = Slider(self.__slider_ax, 'facecolor alpha', np.float64(0), np.float64(255), valinit=125, valstep=5)
self._xdata = 0
self._ydata = 0
self.__checkbox.on_clicked(self.check_box_click)
self.__slider.on_changed(self.slider_change)
self.__fig.canvas.mpl_connect("motion_notify_event", self.on_hover)
def _update_point_annot(self):
self.__point_annot.xy = (self._xdata, self._ydata)
self.__point_annot.set_text("%r , %r" % self.__point_annot.xy)
def on_hover(self, event):
point_annot_vis = self.__point_annot.get_visible()
if event.inaxes == self.__ax:
self._xdata = self.decimal_quantize(event.xdata)
self._ydata = self.decimal_quantize(event.ydata)
self._update_point_annot()
self.__point_annot.set_visible(True)
else:
if point_annot_vis:
self.__point_annot.set_visible(False)
self.__fig.canvas.draw_idle()
def check_box_click(self, label):
self.__ax.set_visible(not self.__ax.get_visible())
self.__fig.canvas.draw_idle()
def slider_change(self, val):
# val is numpy.float64
hex_val = '%02x' % int(val.item())
self.__ax.set_facecolor('#ffffff'+ hex_val)
self.__fig.canvas.draw_idle()
def show_channel_hist(rgb_count):
# takes longer time than show_dimension_plot()
fig = plt.figure('channel histogram', figsize=(10, 6))
ax = plt.axes([0.1, 0.1, 0.65, 0.8])
for i, col in enumerate(('r', 'g', 'b')):
ax.plot(rgb_count[i], color=col, linewidth=1)
rax = plt.axes([0.78, 0.35, 0.2, 0.3])
labels = ['remove_zero', 'remove_maxval']
check = CheckButtons(rax, labels)
flags = {'remove_zero': False, 'remove_maxval': False}
def func(label):
flags[label] = not flags[label]
new_rgb_count = rgb_count.copy()
if flags['remove_zero']:
new_rgb_count[:, 0] = 0
if flags['remove_maxval']:
new_rgb_count[:, -1] = 0
ax.cla()
for i, col in enumerate(('r', 'g', 'b')):
ax.plot(new_rgb_count[i], color=col, linewidth=1)
ax.set_xlim([0, rgb_count.shape[1]])
ax.set_ylim([0, None])
ax.set_xlabel('pixel value')
ax.set_ylabel('frequency')
ax.set_title('channelwise pixel value histogram', pad=30)
plt.show()
check.on_clicked(func)
ax.set_xlim([0, rgb_count.shape[1]])
ax.set_ylim([0, None])
ax.set_xlabel('pixel value')
ax.set_ylabel('frequency')
ax.set_title('channelwise pixel value histogram', pad=30)
plt.show()
def labshow(labels, i=0, images=None, truth=None, test_indices=None):
if images is None:
images = IMAGES
if truth is None:
truth = LABELS
if test_indices is None:
test_indices = TEST
test_indices = np.r_[tuple(test_indices)]
gs = gridspec.GridSpec(GRID_SIZE, GRID_SIZE)
fig = plt.figure()
main_ax = fig.add_subplot(gs[:-1, 1:])
fig.tight_layout()
img_ax = main_ax.imshow(overlay(images[test_indices[i]], labels[i], truth[test_indices[i]]))
time_ax = fig.add_subplot(gs[-1, 1:-1])
time_sld = Slider(time_ax, "Frame", 0, len(test_indices) - .01, valinit=i, valfmt="%d")
half = GRID_SIZE // 2
selector_ax = fig.add_subplot(gs[half-2:half, 0])
selector_chk = CheckButtons(selector_ax, ["Pred", "Truth"], [True, True])
def update(_):
i = int(time_sld.val)
selected = [l1.get_visible() for l1, l2 in selector_chk.lines]
# checkbuttons.get_status() is supposed to exist, but doesn't?
img_ax.set_data(overlay(
images[test_indices[i]],
labels[i] if selected[0] else None,
truth[test_indices[i]] if selected[1] else None))
fig.canvas.draw_idle()
time_sld.on_changed(update)
selector_chk.on_clicked(update)
def onkeypress(event):
if event.key == "left":
time_sld.set_val(max(time_sld.val - 1, time_sld.valmin))
elif event.key == "right":
time_sld.set_val(min(time_sld.val + 1, time_sld.valmax))
elif event.key == "ctrl+left":
time_sld.set_val(max(time_sld.val - 30, time_sld.valmin))
elif event.key == "ctrl+right":
time_sld.set_val(min(time_sld.val + 30, time_sld.valmax))
elif event.key == "home":
time_sld.set_val(time_sld.valmin)
elif event.key == "end":
time_sld.set_val(time_sld.valmax)
fig.canvas.mpl_connect("key_press_event", onkeypress)
plt.show()
def __init__(self, imp_obj):
self.fig = pl.figure()
self.tfmx = imp_obj['TimeFreqImp']
self.fvec = imp_obj['FreqVec']
self.tvec = imp_obj['TimeVec']
self.specs = imp_obj['Specs']
print(self.specs.shape)
self.calibmx = imp_obj['CalibMx']
self.harms = imp_obj['Harms']
self.mode = 'mag'
self.keep_prev = False
self.slice_times = []
self.ax_imped = [
pl.subplot2grid((4, 8), (2, 0), colspan=7),
pl.subplot2grid((4, 8), (3, 0), colspan=7)
]
self.ax_tf = pl.subplot2grid((4, 8), (0, 0), rowspan=2, colspan=7)
self.ax_cbar = pl.subplot2grid((4, 12), (0, 11), colspan=1, rowspan=2)
# Make checkbuttons with all plotted lines with correct visibility
rax = pl.subplot2grid((4, 12), (2, 11), rowspan=2, colspan=2)
check = CheckButtons(rax, ['keep'], [False])
check.on_clicked(self.checkboxes)
self.ax_tf.imshow(
20 * np.log10(np.abs(self.tfmx)),
extent=[0, self.tvec[-1], self.fvec[0], self.fvec[-1]],
aspect='auto',
origin='bottom',
cmap='gray')
pl.colorbar(cax=self.ax_cbar, mappable=self.ax_tf.images[0])
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
self.fig.canvas.mpl_connect('key_press_event', self.onpress)
self.temp_lines = []
self.last_tidx = 0
self.selector = SpanSelector(self.ax_tf,
self.onselect,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5,
facecolor='red'))
self.spans = []
pl.show()
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 interactive_plot_layers(layer_info_type_list,
debug_info_dict_list,
plot_title="untitled"):
fig, ax = plt.subplots()
iters_list = np.unique(
np.array([item.get('NumIters') for item in debug_info_dict_list]))
handles = []
lines = []
plt.xlabel('NumIters')
for (layer_name, column_to_plot) in layer_info_type_list:
row_list = [
item.get(column_to_plot) for item in debug_info_dict_list
if (layer_name == item.get('LayerName')
and item.get(column_to_plot) != None
and not np.isnan(item.get(column_to_plot)))
]
if len(row_list) == 0:
continue
y = np.array(row_list)
line, = ax.plot(iters_list[:len(y)], y, lw=2)
handles.append(layer_name + '.' + column_to_plot)
lines.append(line)
plt.title(plot_title)
plt.subplots_adjust(left=0.4)
rax = plt.axes([0.05, 0.4, 0.3, 0.5])
check = CheckButtons(rax, tuple(handles),
tuple([True for i in range(len(handles))]))
[
rec.set_facecolor(lines[i].get_color())
for i, rec in enumerate(check.rectangles)
]
def func(label):
for i in range(len(handles)):
if label == handles[i]:
lines[i].set_visible(not lines[i].get_visible())
plt.draw()
check.on_clicked(func)
rax._cbutton = check
def graphIt():
print("goin")
#Gets everything started by calculating the datasets before anything is graphed.
euler()
#Graphs the susceptible population.
foxesGraph()
rabbitsGraph()
axcolor = 'white'
rax = plt.axes([0.025, 0.7, 0.18, 0.15], axisbg=axcolor)
radio = CheckButtons(rax, ('Foxes', 'Rabbits'), [True, True])
radio.on_clicked(updateLineType)
ax.grid(True)
ax.axhline(0, color='black', lw=2)
ax.axvline(0, color='black', lw=2)
plt.show()
def slider(self, N):
barpos = plt.axes([0.18, 0.01, 0.70, 0.03], facecolor="gray")
slider = Slider(barpos, '', 0, len(self.lista_hora) - N, valinit=0)
slider.on_changed(self.bar)
rax = plt.axes([0.01, 0.30, 0.12, 0.65])
check = CheckButtons(
rax,
('C. Fund.', 'Arm. 1', 'Arm. 2', 'Arm. 3', 'Arm. 4', 'Arm. 5',
'Arm. 6', 'Arm. 7', 'Arm. 8', 'Arm. 9', 'Arm. 10', 'Arm. 11'),
(True, True, True, True, True, True, True, True, True, True, True,
True))
check.get_status()
check.on_clicked(self.func)
self.bar(0)
plt.show()
def make_start_stop_animation(anim, box=[0.015, 0.05, 0.12, 0.1], start_animation=True):
f = plt.gcf()
ax_btn = f.add_axes(box, facecolor=slider_color)
labels = ['anim']
widget = CheckButtons(ax_btn, labels, [start_animation])
def set_anim(label):
if widget.get_status()[0]:
print('on')
anim.event_source.start()
else:
print('off')
anim.event_source.stop()
widget.on_clicked(set_anim)
return widget
class button_manager:
''' Handles some missing features of matplotlib check buttons
on init:
creates button, links to button_click routine,
calls call_on_click with active index and firsttime=True
on click:
maintains single button on state, calls call_on_click
'''
#@output.capture() # debug
def __init__(self, fig, dim, labels, init, call_on_click):
'''
dim: (list) [leftbottom_x,bottom_y,width,height]
labels: (list) for example ['1','2','3','4','5','6']
init: (list) for example [True, False, False, False, False, False]
'''
self.fig = fig
self.ax = plt.axes(dim) #lx,by,w,h
self.init_state = init
self.call_on_click = call_on_click
self.button = CheckButtons(self.ax, labels, init)
self.button.on_clicked(self.button_click)
self.status = self.button.get_status()
self.call_on_click(self.status.index(True), firsttime=True)
#@output.capture() # debug
def reinit(self):
self.status = self.init_state
self.button.set_active(self.status.index(
True)) #turn off old, will trigger update and set to status
#@output.capture() # debug
def button_click(self, event):
''' maintains one-on state. If on-button is clicked, will process correctly '''
#new_status = self.button.get_status()
#new = [self.status[i] ^ new_status[i] for i in range(len(self.status))]
#newidx = new.index(True)
self.button.eventson = False
self.button.set_active(
self.status.index(True)) #turn off old or reenable if same
self.button.eventson = True
self.status = self.button.get_status()
self.call_on_click(self.status.index(True))
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_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 drawParameterListControls(control_fig,viewer_fig,columns,default_values):
"""
Helper function for adding the check boxes for controlling the parameter list
"""
axcolor = 'w'
char_width = max(map(lambda x:len(x),columns)) #finding the widest column title
rax = plt.axes([0.05, 0.2 + 0.03*len(columns)*2 + 0.1, 0.015*char_width, 0.03*len(columns)], axisbg=axcolor,title="Parameter List")
check = CheckButtons(rax, tuple(columns), ([True]*len(columns)))
def parameterlistChange(label):
global x_label,y_label,parameter_selection,data_dicts
if(label in parameter_selection.keys()): del parameter_selection[label]
else: parameter_selection[label] = default_values[columns.index(label)]
redrawViewFigure(viewer_fig,data_dicts,x_label,y_label,parameter_selection)
check.on_clicked(parameterlistChange)
return check
class ToggleFlags:
def __init__(self, args=None):
self.names = []
self.args = args
def add(self, name, value=False):
if name in ['add', 'showat', '__init__']: return
if name in self.args:
value = True
self.names.append(name)
self.__setattr__(name, value)
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 interactive_plot(plot_pairs_list, x_label, plot_title="untitled", ylim=[]):
fig, ax = plt.subplots()
#iters_list = np.unique(np.array([ item.get('NumIters') for item in debug_info_dict_list]))
handles = []
lines = []
plt.xlabel(x_label)
for x, y_pair in plot_pairs_list:
y, y_label = y_pair
if len(y) == 0:
continue
line, = ax.plot(x[:len(y)], y, lw=2)
handles.append(y_label)
lines.append(line)
if ylim:
ax.set_ylim(ylim)
plt.title(plot_title)
plt.subplots_adjust(left=0.4)
rax = plt.axes([0.05, 0.4, 0.3, 0.5])
check = CheckButtons(rax, tuple(handles),
tuple([True for i in range(len(handles))]))
[
rec.set_facecolor(lines[i].get_color())
for i, rec in enumerate(check.rectangles)
]
def func(label):
for i in range(len(handles)):
if label == handles[i]:
lines[i].set_visible(not lines[i].get_visible())
plt.draw()
check.on_clicked(func)
rax._cbutton = check
def create_graph(self, datas, data_names):
fig, ax = plt.subplots()
# FPS
x_step = 1 / 10
lines = []
for i, data in enumerate(datas):
x = []
for _ in range(len(data)):
x.append(x_step)
x_step += x_step
X = np.array(x)
Y = np.array(data, dtype="float64")
print(len(X))
print(len(Y))
i, = ax.plot(X, Y, label=data_names[i])
lines.append(i)
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 change_visivility(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(change_visivility)
plt.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()
def scmoplot(root_path, user_ps):
ps = dict(default_ps)
ps.update(user_ps)
ng = NameGleaner(scan=r'scan=(\d+)', x=r'x=(\d+)', y=r'y=(\d+)',
averaged=r'(averaged)')
tfmr = Transformer(gleaner=ng)
tfmr.add(10, tfms.scale, params={'xsc': 0.1})
tfmr.add(20, tfms.flatten_saturation,
params={'threshold': ps['thresh'], 'polarity': '+'})
tfmr.add(25, tfms.center)
tfmr.add(30, tfms.wrapped_medfilt, params={'ks': ps['filt_ks']})
tfmr.add(40, tfms.saturation_normalize, params={'thresh': ps['thresh']})
tfmr2 = Transformer(gleaner=ng)
tfmr2.add(10, tfms.scale, params={'xsc': 0.1})
tfmr2.add(30, tfms.wrapped_medfilt, params={'ks': ps['filt_ks']})
tfmr2.add(40, tfms.clean)
clust = Cluster(join(root_path, 'parameters.xml')).to_dict()
gx, gy = (clust['Rows'], clust['Cols'])
fig, axarr = plt.subplots(ncols=gx, nrows=gy,
figsize=(10, 10))
for row in axarr:
for ax in row:
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
#ax.set_xlim(-ps['xlim'], ps['xlim'])
#ax.set_ylim(-ps['ylim'], ps['ylim'])
Hcs = [[None for i in range(gx)] for i in range(gy)]
Mrs = [[None for i in range(gx)] for i in range(gy)]
for f in listdir(root_path):
gleaned = ng.glean(f)
if gleaned['averaged']:
print('Plotting %s' % f)
x, y = int(gleaned['x']), int(gleaned['y'])
ax = axarr[y, x]
Bi, Vi = np.loadtxt(join(root_path, f), usecols=(0, 1), unpack=True,
skiprows=7)
B,V = tfmr((Bi,Vi),f)
B2, V2 = tfmr2((Bi, Vi), f)
##data set 2 graphs
lslope,rslope,tan=tfms.x0slope(B2,V2)
lsat,rsat=tfms.sat_field(B2,V2)
area = tfms.loop_area(B2,V2)
data = ax.plot(B2,V2,'k')
tanlines = ax.plot(tan[0],tan[1],'r',tan[2],tan[3],'y*',tan[4],tan[5],'b',tan[6],tan[7], 'y*')
satfields = ax.plot(B2[lsat],V2[lsat],'ro',B2[rsat],V2[rsat],'go')
areatext = ax.text(B2.min(),V2.max(), ("loop area: "+str(area+.0005)[0:6]))
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
check = CheckButtons(rax, ('data', 'tangent lines',
'saturation points', 'loop area'), (True, True, True, True))
def func(label):
if label == 'data': toggle(data)
elif label == 'tangent lines': toggle(tanlines)
elif label == 'saturation points': toggle(satfields)
elif label == 'loop area': areatext.set_visible(not areatext.get_visible())
plt.draw()
check.on_clicked(func)
try:
Hc = tfms.Hc_of(B, V, fit_int=(ps['thresh'], ps['max']))
Hcs[y][x] = Hc
Mr = tfms.Mrem_of(B, V, fit_int=(ps['thresh'], ps['max']))
Mrs[y][x] = Mr
zs = np.zeros(3)
ax.plot(zs, Mr, 'ro', ms=7)
ax.plot(Hc, zs, 'ro', ms=7)
except Exception as e:
print('\t{}'.format(e))
Hcs[y][x] = 0.0
Mrs[y][x] = 0.0
plt.tight_layout(w_pad=0, h_pad=0)
plt.show()
Hcs = np.array([x[1] for row in Hcs for x in row]).reshape(gy, gx)
Mrs = np.array([x[1] for row in Mrs for x in row]).reshape(gy, gx)
gs = GridSpec(10, 10)
ax0 = plt.subplot(gs[0:9, :5])
ax1 = plt.subplot(gs[9, :5])
ax2 = plt.subplot(gs[0:9, 5:])
ax3 = plt.subplot(gs[9, 5:])
fig = ax0.get_figure()
fig.set_size_inches(12, 8)
# Plot Hc pcolor map
n = Normalize(vmin=0.0, vmax=5.0, clip=True)
res = ax0.pcolor(Hcs, cmap='afmhot', norm=n, edgecolors='k')
plt.colorbar(res, cax=ax1, orientation='horizontal', ticks=(0, 2.5, 5))
# Plot Mr pcolor map
n = Normalize(vmin=0.0, vmax=1.0, clip=True)
res = ax2.pcolor(Mrs, cmap='afmhot', norm=n, edgecolors='k')
plt.colorbar(res, cax=ax3, orientation='horizontal', ticks=(0, 0.5, 1))
ax0.set_title('Hc (mT)')
ax0.set_aspect('equal', adjustable='box')
ax2.set_title('Mrem/Msat')
ax2.set_aspect('equal', adjustable='box')
plt.tight_layout()
plt.show()
Bu.lines[0][0].set_ydata([CheckButtonsCrossU,CheckButtonsCrossD])
Bu.lines[0][1].set_xdata([CheckButtonsCrossL,CheckButtonsCrossR])
Bu.lines[0][1].set_ydata([CheckButtonsCrossD,CheckButtonsCrossU])
left_In = left_start
bottom_In = bottom_save
height_In = height_save*0.4
width_In = 0.3
axIn = plt.axes([left_In, bottom_In, width_In, height_In])
BuIn = CheckButtons(axIn, ('Interpolate',), (False,))
AdjustCheckButton(BuIn)
Interpolate=False
def helper_interpolate(val):
global Interpolate
Interpolate=not Interpolate
BuIn.on_clicked(helper_interpolate)
left_SLy = left_start
bottom_SLy= bottom_save+1.2*height_In
height_SLy= height_In
width_SLy = 0.3
axSLy= plt.axes([left_SLy, bottom_SLy, width_SLy, height_SLy])
BuSLy= CheckButtons(axSLy, ('Log y',), (False,))
AdjustCheckButton(BuSLy)
SLyPlot=False
def helper_slyplot(val):
global SLyPlot
SLyPlot=not SLyPlot
update(0)
BuSLy.on_clicked(helper_slyplot)
#gausspl,lorenzpl,hmpl,hm2pl=plot(rx, [Gauss(x-xmed)/f0 for x in rx], 'r', rx, [Lorenz(x-xmed)/f0 for x in rx], 'b', [xmed-hw/2.,xmed+hw/2.],[0.5,0.5], 'g', [xmed-hw/2.,xmed+hw/2.],[0.5*l0/f0,0.5*l0/f0], 'g')
#grid(True)
#ylabel('Response')
#xlabel('Frequency [THz]')
subplots_adjust(bottom=0.25, right=0.95)
axcolor = 'lightgoldenrodyellow'
axfwhmG = axes([0.1, 0.15, 0.6, 0.03], axisbg=axcolor)
fwhmG = Slider(axfwhmG, 'Gauss', 5*step, xrng/2, valinit=hw)
fwhmG.on_changed(updateG)
axfwhmL = axes([0.1, 0.05, 0.6, 0.03], axisbg=axcolor)
fwhmL = Slider(axfwhmL, 'Lorentz', 5*step, xrng/2, valinit=hw)
fwhmL.on_changed(updateL)
active=[True,False,True,True]
lpl.set_visible(False)
cbData=CheckButtons(axes([0.78, 0.05, 0.2, 0.15]),('Gauss','Lorenz','Theory','Experiment'),active)
cbData.on_clicked(changeData)
show()
for k in range(len(cx)) :
print cx[k], cg[k], cl[k]
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]
fuel_buckets = [0,50,100,150,200,250,300,350,400,450,500,550,600,650,700,750,800,850,900,950,1000.0,1050,1100,1150,1200,1250,1300,1350,1400,1450,1500,1550,1600,1650,1700,1750,1800,1850,1900,1950,2000]
aux_aux = figsrc.add_subplot(224)
aux_aux.hist(dist, bins = buckets)
title("Distance")
xlabel("Distance (km)")
ylabel("Number of People")
b_pace.on_clicked(to_pace)
b_dist.on_clicked(to_dist)
b_dur.on_clicked(to_dur)
b_cal.on_clicked(to_cal)
b_fuel.on_clicked(to_fuel)
b_reset.on_clicked(cluster_man.reset_area)
b_reclust.on_clicked(cluster_man.Cluster)
check.on_clicked(cluster_man)
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 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])
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)
# Write gnuplot script
def run_main(
A,
B,
C,
dA,
dB,
dC,
DJ,
DJK,
DK,
dJ,
dK,
ua,
ub,
uc,
dua,
dub,
duc,
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, triples_flag
triples_flag = 0
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 trans_1, trans_2, trans_3, peak_list
trans_1 = []
trans_2 = []
trans_3 = []
peak_list = []
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=0.925)
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")
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([0.935, 0.32, 0.01, 0.6], axisbg=axcolor)
axub = plt.axes([0.96, 0.32, 0.01, 0.6], axisbg=axcolor)
axuc = plt.axes([0.985, 0.32, 0.01, 0.6], 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)
dua_2 = dua
dub_2 = dub
duc_2 = duc
if (ua_g - dua) < 0:
dua_2 = ua_g
if (ub_g - dub) < 0:
dub_2 = ub_g
if (uc_g - duc) < 0:
duc_2 = uc_g
global ua_slider
ua_slider = VSlider(axua, "u a", ua_g - dua_2, ua_g + dua, valinit=ua_g)
ua_slider.on_changed(update)
global ub_slider
ub_slider = VSlider(axub, "u b", ub_g - dub_2, ub_g + dub, valinit=ub_g)
ub_slider.on_changed(update)
global uc_slider
uc_slider = VSlider(axuc, "u c", uc_g - duc_2, uc_g + duc, 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 display(self):
"""Method to create the plotting window and fill it with buttons."""
# Create matplotlib window dressing
self.fig = plt.figure(figsize=(11,9), facecolor='lightgoldenrodyellow')
#self.fig = plt.figure(facecolor='lightgoldenrodyellow')
self.fig.canvas.set_window_title('PyPop: Visualization')
self.fig.clear()
# put some buttons on the figure for the plot axes
buttonW = 0.055
buttonH = 0.045 # default button sizes
# arguments are button left, bottom, width, height and label
# check box to do log scale
buttonXLog = CheckButtons(plt.axes([0.001, .3, 1.5*buttonW, 1.5*buttonH]),
['log x'],
actives=[False])
self.xlog = False
buttonYLog = CheckButtons(plt.axes([0.001+2.5*buttonW, .3,
1.5*buttonW, 1.5*buttonH]),
['log y'],
actives=[False])
self.ylog = False
# button to do plot
buttonPlot = Button(plt.axes([0.001+2.0*buttonW, 0.2, 1.5*buttonW, buttonH]),
'Plot')
self.fig.text(0.003, 0.98, 'PLOT SELECTION')
self.fig.text(0.003, 0.95, 'X Axis')
self.fig.text(0.003, 0.65, 'Y Axis')
# radio buttons for x and y axis values
radioXAxis = RadioButtons(plt.axes([0.001,
0.72,
len(self.textlabels)*0.02,
len(self.textlabels)*0.02]),
self.textlabels,
active=0,
activecolor='blue')
self.xindex = 0
radioYAxis = RadioButtons(plt.axes([0.001,
0.42,
len(self.textlabels)*0.02,
len(self.textlabels)*0.02]),
self.textlabels,
active=1,
activecolor='blue')
self.yindex = 1
# if radio button is clicked, change active X/Y index
radioXAxis.on_clicked(self._radioXClick)
radioYAxis.on_clicked(self._radioYClick)
# add callback to the plot button(s?)
buttonPlot.on_clicked(self._scatterplot)
# callbacks for the log plot switches
buttonXLog.on_clicked(self._logClick)
buttonYLog.on_clicked(self._logClick)
plt.show()
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_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 HilbertGUI(object):
def __init__(self, config, debug=False):
"""
:param config:
If a string, then treat it as a filename of a YAML config file; if
a dictionary then treat it as the config dictionary itself.
For each dictionary in `config['data']`, a new matrix, colorbar,
and slider will be created using the filename and colormap
specified. The matrices for the files will be plotted on the same
Axes.
There is no limit, but colors get complicated quickly
with, say, >3 files.
Example config dict::
{
'dim': 128,
'genome': 'hg19',
'chrom': 'chr10',
'data': [
{'filename': '../data/cpg-islands.hg19.chr10.bed',
'colormap': 'Blues'},
{'filename': '../data/refseq.chr10.exons.bed',
'colormap': 'Reds'}
]
}
Example YAML file::
dim: 128
chrom: chr10
genome: hg19
data:
-
filename: ../data/cpg-islands.hg19.chr10.bed
colormap: Blues
-
filename: ../data/refseq.chr10.exons.bed
colormap: Reds
:param debug:
If True, then print some extra debugging info
:param kwargs:
Additional keyword arguments are passed to HilbertMatrix (e.g.,
m_dim, genome, chrom)
"""
self.config = self._parse_config(config)
self.matrix_dim = self.config['dim']
kwargs = dict(
matrix_dim=self.config['dim'],
genome=self.config['genome'],
chrom=self.config['chrom'])
self.hilberts = []
self.colormaps = []
for chunk in self.config['data']:
self.hilberts.append(HilbertMatrix(chunk['filename'], **kwargs))
self.colormaps.append(getattr(matplotlib.cm, chunk['colormap']))
for h in self.hilberts:
h.mask_low_values()
self.debug = debug
self.n = len(self.config['data'])
self.fig = plt.figure(figsize=(8, 8))
def _parse_config(self, config):
if isinstance(config, basestring):
config = yaml.load(open(config))
self._validate_config(config)
return config
def _validate_config(self, config):
# TODO: more work on validation
assert 'data' in config
def _make_main_axes(self):
self.ax = plt.Axes(self.fig, (0.1, 0.1, 0.8, 0.8))
# formatting: remove ticklabels on the main axes since they're not
# meaningful
self.ax.set_xticks([])
self.ax.set_yticks([])
self.divider = make_axes_locatable(self.ax)
self.fig.add_axes(self.ax)
def _make_colorbar_axes(self):
cax_total_width = 0.4
cax_total_padding = 0.8
width = cax_total_width / self.n
pad = cax_total_padding / self.n
self.caxes = []
for i in range(self.n):
self.caxes.append(
self.divider.append_axes('right', size=width, pad=pad))
def _make_alpha_slider_axes(self):
# Alpha sliders.
self.slider_axes = []
for i in range(self.n):
self.slider_axes.append(
self.divider.append_axes('bottom', size=0.1, pad=0.1))
def _make_min_slider_axes(self):
return
self.min_slider_ax1 = plt.Axes(self.fig, (0.7, 0.07, 0.07, 0.02))
self.min_slider_ax2 = plt.Axes(self.fig, (0.7, 0.02, 0.07, 0.02))
self.fig.add_axes(self.min_slider_ax1)
self.fig.add_axes(self.min_slider_ax2)
def _make_annotation_axes(self):
self.annotation_ax = plt.Axes(
self.fig, (0.1, 0.9, 0.8, 0.05), frame_on=False)
self.annotation_ax.set_xticks([])
self.annotation_ax.set_yticks([])
# necessary to capture the canvas before drawing new things to it.
# otherwise, old chrom coordinates are left on the canvas.
#http://stackoverflow.com/questions/6286731/ \
#animating-matplotlib-panel-blit-leaves-old-frames-behind
self.fig.canvas.draw()
self.background = self.fig.canvas.copy_from_bbox(
self.annotation_ax.bbox)
self.current_position_label = self.annotation_ax.text(
.5, .5, 'position...', horizontalalignment='center',
verticalalignment='center', size=10, animated=True)
self.fig.add_axes(self.annotation_ax)
def _make_checkbox_axes(self):
self.check_ax = plt.Axes(self.fig, (0.02, 0.02, 0.6, 0.1))
self.fig.add_axes(self.check_ax)
def _make_radio_axes(self):
self.radio_ax = plt.Axes(self.fig, (0.85, 0.02, 0.1, 0.1))
self.fig.add_axes(self.radio_ax)
def _imshow_matrices(self):
self.mappables = []
for i in range(self.n):
h = self.hilberts[i]
cmap = self.colormaps[i]
if i == 0:
picker = 5
else:
picker = None
self.mappables.append(
self.ax.imshow(
h.masked, interpolation='nearest', origin='upper',
cmap=cmap, picker=picker))
# Initialize alphas
for m in self.mappables:
m.set_alpha(0.5)
def _matrix_colorbars(self):
# colorbars
self.cbars = []
for i in range(self.n):
m = self.mappables[i]
cax = self.caxes[i]
self.cbars.append(plt.colorbar(m, cax=cax))
# Tweak colorbar labels
for cbar in self.cbars:
for txt in cbar.ax.get_yticklabels():
txt.set_size(8)
def _init_alpha_sliders(self):
# Set up sliders with sensible default labels
self.sliders = []
for i in range(self.n):
fn = self.config['data'][i]['filename']
label = '%s: %s' % (string.letters[i], os.path.basename(fn))
slider = Slider(
self.slider_axes[i],
label,
valmin=0,
valmax=1,
valinit=0.5)
slider.label.set_size(10)
self.sliders.append(slider)
#self.slider1.poly.set_color('#7a0510')
#self.slider2.poly.set_color('#08316d')
def _init_min_sliders(self):
return
self.min_slider1 = Slider(
self.min_slider_ax1,
'min',
valmin=self.h1.masked.min(),
valmax=self.h1.masked.max(),
valinit=0)
self.min_slider2 = Slider(
self.min_slider_ax2,
'min',
valmin=self.h2.masked.min(),
valmax=self.h2.masked.max(),
valinit=0)
self.min_slider1.poly.set_color('#7a0510')
self.min_slider2.poly.set_color('#08316d')
self.min_slider1.label.set_size(10)
self.min_slider2.label.set_size(10)
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 _init_radio(self):
# For controlling log/linear color scale
self.radio = RadioButtons(
self.radio_ax,
labels=['log', 'linear'],
active=1)
def _make_connections(self):
# Alpha sliders
for i in range(self.n):
self.sliders[i].on_changed(
self._slider_callback_factory(
self.mappables[i], self.cbars[i]))
"""
# Min sliders change thresh
self.min_slider1.on_changed(
self._min_slider_callback_factory(
self.h1, self.mappable1, self.cbar1))
self.min_slider2.on_changed(
self._min_slider_callback_factory(
self.h2, self.mappable2, self.cbar2))
"""
# Radio callback changes color scale
self.radio.on_clicked(self._radio_callback)
#Checkbox callback changes what hilberts are shown
self.checks.on_clicked(self._check_callback)
self.fig.canvas.mpl_connect('motion_notify_event', self._coord_tracker)
self.fig.canvas.mpl_connect('pick_event', self._coord_callback)
def plot(self):
"""
Does most of the work to set up the figure, axes, and widgets.
"""
# These methods construct axes in the right places
self._make_main_axes()
self._make_colorbar_axes()
self._make_alpha_slider_axes()
self._make_min_slider_axes()
self._make_annotation_axes()
self._make_radio_axes()
self._make_checkbox_axes()
# Plot the matrices and their colorbars
self._imshow_matrices()
self._matrix_colorbars()
# Initialize the various widgets
self._init_alpha_sliders()
self._init_min_sliders()
self._init_radio()
self._init_checks()
# Connect callbacks to events
self._make_connections()
def _coord_tracker(self, event):
"""
Callback that updates text based on the genomic coords of the current
mouse position.
"""
# Restore original background
self.fig.canvas.restore_region(self.background)
# These will be None if the mouse is not in an Axes, so the string
# should be empty.
#
# Also, make sure we're in the imshow Axes -- don't want crazy genomic
# coords from being in the colorbar or annotation Axes
x = event.xdata
y = event.ydata
if (x is None) or (y is None) or (event.inaxes is not self.ax):
s = ""
# Get matrix coords
else:
xi = int(round(x))
yi = int(round(y))
# If you move the mouse off the edge of the main Axes, rounding to
# the nearest row or col may get you a value that's greater than
# the number of rows/cols. In this case, treat it similar to being
# out of the Axes, with an empty string.
if (xi >= self.matrix_dim) or (yi >= self.matrix_dim):
s = ""
else:
# Genomic coords from (x,y)
s = '%s:%s-%s' % self.hilberts[0].xy2chrom(xi, yi)
v = []
for letter, h in zip(string.letters, self.hilberts):
v.append('%s=%s' % (letter, h.matrix[yi, xi]))
s += self._xy_to_value_string(xi, yi)
# Update text, redraw just the text object, and blit the background
# previously saved
self.current_position_label.set_text(s)
self.annotation_ax.draw_artist(self.current_position_label)
self.fig.canvas.blit(self.annotation_ax.bbox)
def _xy_to_value_string(self, x, y):
v = []
for letter, h in zip(string.letters, self.hilberts):
v.append('%s=%s' % (letter, h.matrix[y, x]))
return '; '.join(v)
def _coord_callback(self, event):
x = event.mouseevent.xdata
y = event.mouseevent.ydata
xi = int(round(x))
yi = int(round(y))
if self.debug:
print
print 'mouse x:', x, 'xi:', xi
print 'mouse y:', y, 'yi:', yi
s = '%s:%s-%s' % self.hilberts[0].xy2chrom(xi, yi)
s += '\n' + self._xy_to_value_string(xi, yi)
print s
sys.stdout.flush()
def _radio_callback(self, label):
# Hello? Am I the 9th caller!?
if label == 'log':
self._log()
elif label == 'linear':
self._linear()
else:
raise ValueError("unspecified label for radio button")
def _check_callback(self, label):
for i in xrange(len(self.check_labels)):
if label == self.check_labels[i]:
self.mappables[i].set_visible(not \
self.mappables[i].get_visible())
plt.draw()
def _slider_callback_factory(self, mappable, cbar):
"""
Given a mappable (i.e., object returned from imshow()), return
a function that will modify it based on the slider's value.
"""
def _slider_callback(x):
mappable.set_alpha(x)
cbar.set_alpha(x)
cbar.update_normal(mappable)
return _slider_callback
def _min_slider_callback_factory(self, h, mappable, cbar):
def _min_slider_callback(x):
h.mask_low_values(min_val=x)
mappable.set_data(h.masked)
return _min_slider_callback
def _log(self):
"""
Update colomaps of the plotted images to use log-scaled color
"""
for i in range(self.n):
norm = matplotlib.colors.LogNorm(
vmin=self.hilberts[i].masked.min(),
vmax=self.hilberts[i].masked.max())
self.mappables[i].set_norm(norm)
self.cbars[i].set_norm(norm)
self.cbars[i].update_normal(self.mappables[i])
plt.draw()
def _linear(self):
"""
Update colormaps of the plotted images to use linear-scaled color
"""
for i in range(self.n):
norm = matplotlib.colors.Normalize(
vmin=self.hilberts[i].masked.min(),
vmax=self.hilberts[i].masked.max())
self.mappables[i].set_norm(norm)
self.cbars[i].set_norm(norm)
self.cbars[i].update_normal(self.mappables[i])
plt.draw()
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 run_main(
A,
B,
C,
dA,
dB,
dC,
DJ,
DJK,
DK,
dJ,
dK,
ua,
ub,
uc,
f_lower,
f_upper,
T,
last_times_picked_list,
number_of_skips,
spectrum_filename=None,
peaklist=None,
):
# 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
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)
a0 = 5
f0 = 3
global l
l, = plt.plot(t, s, lw=2, color="red")
ax.set_xlim([f_lower_g, f_upper_g])
global picked_plt
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)
try:
ax2.scatter(peaklist[:, 0], peaklist[:, 1], color="red")
ax2.set_xlim([f_lower_g, f_upper_g])
except TypeError or IOError:
pass
try:
fh = open(spectrum_filename)
spectrum_list_F = []
spectrum_list_I = []
counter = 0
for line in fh:
counter += 1
if counter % (number_of_skips + 1) == 0:
spectrum_list_F.append(line.split()[0])
spectrum_list_I.append(line.split()[1])
else:
pass
ax2.plot(spectrum_list_F, spectrum_list_I, lw=2, color="black")
ax2.set_xlim([f_lower_g, f_upper_g])
except TypeError or IOError:
pass
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)
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, 6, "")
text_box2 = plt.text(
-1,
23,
"Refine Mouse Selection: Select transitions by pushing 'q' and then clicking in the predicted spectrum ",
)
plt.show()
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()
valinit=model.params["phi0caret"], color='#AAAAAA')
slider6.on_changed(model.on_changephi0caret)
# Pressure slider
slider_ax = plt.axes([0.1, 0.13, 0.5, 0.02])
slider7 = Slider(slider_ax, r"$p$", 100., 975., \
valinit=model.params["p"], color='#AAAAAA')
slider7.on_changed(model.on_changep)
# Visibility Buttons
check_ax = plt.axes([0.66, 0.02, 0.06, 0.15])
labels = [f.label for f in model.fields[:5]]
vis = [f.visibility for f in model.fields[:5]]
check1 = CheckButtons(check_ax, labels, vis)
check1.on_clicked(model.select_vis)
check_ax = plt.axes([0.71, 0.02, 0.1, 0.15], aspect='equal')
labels = [f.label for f in model.fields[5:10]]
vis = [f.visibility for f in model.fields[5:10]]
check2 = CheckButtons(check_ax, labels, vis)
check2.on_clicked(model.select_vis)
check_ax = plt.axes([0.79, 0.02, 0.1, 0.15], aspect='equal')
labels = [f.label for f in model.fields[10:]]
vis = [f.visibility for f in model.fields[10:]]
check3 = CheckButtons(check_ax, labels, vis)
check3.on_clicked(model.select_vis)
plt.show()
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()
#if __name__=='__main__':
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 ")
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
redraw_curve()
def updateLAG(label):
global fg, ax, p1, p2, mylag, myMOCsel
mylag = int(label);
print('shifting MOC to lag={}'.format(mylag))
draw_pcolor();
redraw_curve()
def filterpval(label):
global stat_pvalfilter, cb
stat_pvalfilter = not stat_pvalfilter
draw_pcolor();
# update widgets
fg.canvas.mpl_connect('button_press_event', onpick_xcorrBSFidx)
RBlag.on_clicked(updateLAG)
CBpval.on_clicked(filterpval)
# -----------------------------------------------------------------------------
# moving min/max of BSFidx
fg, ax = spsinv();
p00 = ax.pcolor(lat_auxgrd, zT, mxcorrBSFidx[:,:,idxlag0])
p01 = ax.plot(fminx, fminy, 'o-', prop_of_min);
p02 = ax.plot(fmaxx, fmaxy, 'o-', prop_of_max);
ax.legend()
# -----------------------------------------------------------------------------
# movie of BSFidx over lags with max/min as dots
writer = utils_plt.movie_setup()
fg, ax = spsinv(); cb00 = 'dummy'
with writer.saving(fg, path_figscorr+'xcorrBSFidx_201.mp4', 100): # 100 dpi
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()
def ishow(self):
"""
interactive show of trajectories
Examples
--------
.. plot::
:include-source:
>>> from pylayers.mobility.trajectory import *
>>> T=Trajectories()
>>> T.loadh5()
>>> T.ishow()
"""
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2, left=0.3)
t = np.arange(0, len(self[0].index), self[0].ts)
L = Layout(self.Lfilename)
fig, ax = L.showG('s', fig=fig, ax=ax)
valinit = 0
lines = []
labels = []
colors = "bgrcmykw"
for iT, T in enumerate(self):
if T.typ == 'ag':
lines.extend(ax.plot(T['x'][0:valinit],T['y'][0:valinit], 'o',
color=colors[iT], visible=True))
labels.append(T.name + ':' + T.ID)
else:
lines.extend(ax.plot(T['x'][0], T['y'][0], '^', ms=12,
color=colors[iT], visible=True))
labels.append(T.name + ':' + T.ID)
t = self[0].time()
# init boolean value for visible in checkbutton
blabels = [True]*len(labels)
########
# slider
########
slider_ax = plt.axes([0.1, 0.1, 0.8, 0.02])
slider = Slider(slider_ax, "time", self[0].tmin, self[0].tmax,
valinit=valinit, color='#AAAAAA')
def update(val):
if val >= 1:
pval=np.where(val>t)[0]
ax.set_title(str(self[0].index[pval[-1]].time())[:11].ljust(12),
loc='left')
for iT, T in enumerate(self):
if T.typ == 'ag':
lines[iT].set_xdata(T['x'][pval])
lines[iT].set_ydata(T['y'][pval])
fig.canvas.draw()
slider.on_changed(update)
########
# choose
########
rax = plt.axes([0.02, 0.5, 0.3, 0.2], aspect='equal')
# check (ax.object, name of the object , bool value for the obsject)
check = CheckButtons(rax, labels, tuple(blabels))
def func(label):
i = labels.index(label)
lines[i].set_visible(not lines[i].get_visible())
fig.canvas.draw()
check.on_clicked(func)
fig.canvas.draw()
plt.show(fig)
