def test_selector_clear_method(selector):
ax = get_ax()
def onselect(*args):
pass
if selector == 'span':
tool = widgets.SpanSelector(ax, onselect, 'horizontal',
interactive=True,
ignore_event_outside=True)
else:
tool = widgets.RectangleSelector(ax, onselect, interactive=True)
click_and_drag(tool, start=(10, 10), end=(100, 120))
assert tool._selection_completed
assert tool.visible
if selector == 'span':
assert tool.extents == (10, 100)
tool.clear()
assert not tool._selection_completed
assert not tool.visible
# Do another cycle of events to make sure we can
click_and_drag(tool, start=(10, 10), end=(50, 120))
assert tool._selection_completed
assert tool.visible
if selector == 'span':
assert tool.extents == (10, 50)
def check_span(*args, **kwargs):
fig, ax = plt.subplots(1, 1)
ax.plot([0, 200], [0, 200])
ax.figure.canvas.draw()
def onselect(vmin, vmax):
ax._got_onselect = True
assert vmin == 100
assert vmax == 150
def onmove(vmin, vmax):
assert vmin == 100
assert vmax == 125
ax._got_on_move = True
if 'onmove_callback' in kwargs:
kwargs['onmove_callback'] = onmove
tool = widgets.SpanSelector(ax, onselect, *args, **kwargs)
event = get_event(ax, xdata=100, ydata=100, button=1)
tool.press(event)
event = get_event(ax, xdata=125, ydata=125, button=1)
tool.onmove(event)
event = get_event(ax, xdata=150, ydata=150, button=1)
tool.release(event)
assert ax._got_onselect
if 'onmove_callback' in kwargs:
assert ax._got_on_move
def test_span_selector_onselect(interactive):
# check when press and release events take place at the same position
ax = get_ax()
def onselect(vmin, vmax):
ax._got_onselect = True
tool = widgets.SpanSelector(ax,
onselect,
'horizontal',
interactive=interactive)
do_event(tool, 'press', xdata=100, ydata=100, button=1)
# move outside of axis
do_event(tool, 'onmove', xdata=150, ydata=100, button=1)
do_event(tool, 'release', xdata=150, ydata=100, button=1)
assert tool.ax._got_onselect
assert tool.extents == (100, 150)
# Reset tool.ax._got_onselect
tool.ax._got_onselect = False
do_event(tool, 'press', xdata=10, ydata=100, button=1)
do_event(tool, 'release', xdata=10, ydata=100, button=1)
assert tool.ax._got_onselect
def span_selection_init(self):
self.span = mwidgets.SpanSelector(self.axes,
self.on_select_span,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5,
facecolor='red'))
def test_span_selector_drag(drag_from_anywhere):
ax = get_ax()
def onselect(*args):
pass
# Create span
tool = widgets.SpanSelector(ax, onselect, 'horizontal', interactive=True,
drag_from_anywhere=drag_from_anywhere)
click_and_drag(tool, start=(10, 10), end=(100, 120))
assert tool.extents == (10, 100)
# Drag inside span
#
# If drag_from_anywhere == True, this will move the span by 10,
# giving new value extents = 20, 110
#
# If drag_from_anywhere == False, this will create a new span with
# value extents = 25, 35
click_and_drag(tool, start=(25, 15), end=(35, 25))
if drag_from_anywhere:
assert tool.extents == (20, 110)
else:
assert tool.extents == (25, 35)
# Check that in both cases, dragging outside the span draws a new span
click_and_drag(tool, start=(175, 185), end=(185, 195))
assert tool.extents == (175, 185)
def test_span_selector_set_props_handle_props():
ax = get_ax()
def onselect(epress, erelease):
pass
tool = widgets.SpanSelector(ax,
onselect,
'horizontal',
interactive=True,
props=dict(facecolor='b', alpha=0.2),
handle_props=dict(alpha=0.5))
# Create rectangle
do_event(tool, 'press', xdata=0, ydata=10, button=1)
do_event(tool, 'onmove', xdata=100, ydata=120, button=1)
do_event(tool, 'release', xdata=100, ydata=120, button=1)
artist = tool._selection_artist
assert artist.get_facecolor() == mcolors.to_rgba('b', alpha=0.2)
tool.set_props(facecolor='r', alpha=0.3)
assert artist.get_facecolor() == mcolors.to_rgba('r', alpha=0.3)
for artist in tool._handles_artists:
assert artist.get_color() == 'b'
assert artist.get_alpha() == 0.5
tool.set_handle_props(color='r', alpha=0.3)
for artist in tool._handles_artists:
assert artist.get_color() == 'r'
assert artist.get_alpha() == 0.3
def test_span_selector_ignore_outside(ignore_event_outside):
ax = get_ax()
def onselect(vmin, vmax):
ax._got_onselect = True
def onmove(vmin, vmax):
ax._got_on_move = True
tool = widgets.SpanSelector(ax, onselect, 'horizontal',
onmove_callback=onmove,
ignore_event_outside=ignore_event_outside)
click_and_drag(tool, start=(100, 100), end=(125, 125))
assert ax._got_onselect
assert ax._got_on_move
assert tool.extents == (100, 125)
# Reset
ax._got_onselect = False
ax._got_on_move = False
# Trigger event outside of span
click_and_drag(tool, start=(150, 150), end=(160, 160))
if ignore_event_outside:
# event have been ignored and span haven't changed.
assert not ax._got_onselect
assert not ax._got_on_move
assert tool.extents == (100, 125)
else:
# A new shape is created
assert ax._got_onselect
assert ax._got_on_move
assert tool.extents == (150, 160)
def test_span_selector_ignore_outside(ignore_event_outside):
ax = get_ax()
def onselect(vmin, vmax):
ax._got_onselect = True
def onmove(vmin, vmax):
ax._got_on_move = True
tool = widgets.SpanSelector(ax, onselect, 'horizontal',
onmove_callback=onmove,
ignore_event_outside=ignore_event_outside)
do_event(tool, 'press', xdata=100, ydata=100, button=1)
do_event(tool, 'onmove', xdata=125, ydata=125, button=1)
do_event(tool, 'release', xdata=125, ydata=125, button=1)
assert ax._got_onselect
assert ax._got_on_move
assert tool.extents == (100, 125)
# Reset
ax._got_onselect = False
ax._got_on_move = False
# Trigger event outside of span
do_event(tool, 'press', xdata=150, ydata=150, button=1)
do_event(tool, 'onmove', xdata=160, ydata=160, button=1)
do_event(tool, 'release', xdata=160, ydata=160, button=1)
if ignore_event_outside:
# event have been ignored and span haven't changed.
assert not ax._got_onselect
assert not ax._got_on_move
assert tool.extents == (100, 125)
else:
# A new shape is created
assert ax._got_onselect
assert ax._got_on_move
assert tool.extents == (150, 160)
def test_span_selector_drag(drag_from_anywhere):
ax = get_ax()
def onselect(*args):
pass
# Create span
tool = widgets.SpanSelector(ax, onselect, 'horizontal', interactive=True,
drag_from_anywhere=drag_from_anywhere)
do_event(tool, 'press', xdata=10, ydata=10, button=1)
do_event(tool, 'onmove', xdata=100, ydata=120, button=1)
do_event(tool, 'release', xdata=100, ydata=120, button=1)
assert tool.extents == (10, 100)
# Drag inside span
#
# If drag_from_anywhere == True, this will move the span by 10,
# giving new value extents = 20, 110
#
# If drag_from_anywhere == False, this will create a new span with
# value extents = 25, 35
do_event(tool, 'press', xdata=25, ydata=15, button=1)
do_event(tool, 'onmove', xdata=35, ydata=25, button=1)
do_event(tool, 'release', xdata=35, ydata=25, button=1)
if drag_from_anywhere:
assert tool.extents == (20, 110)
else:
assert tool.extents == (25, 35)
# Check that in both cases, dragging outside the span draws a new span
do_event(tool, 'press', xdata=175, ydata=185, button=1)
do_event(tool, 'onmove', xdata=185, ydata=195, button=1)
do_event(tool, 'release', xdata=185, ydata=195, button=1)
assert tool.extents == (175, 185)
def test_span_selector_bound(direction):
fig, ax = plt.subplots(1, 1)
ax.plot([10, 20], [10, 30])
ax.figure.canvas.draw()
x_bound = ax.get_xbound()
y_bound = ax.get_ybound()
tool = widgets.SpanSelector(ax, print, direction, interactive=True)
assert ax.get_xbound() == x_bound
assert ax.get_ybound() == y_bound
bound = x_bound if direction == 'horizontal' else y_bound
assert tool._edge_handles.positions == list(bound)
press_data = [10.5, 11.5]
move_data = [11, 13] # Updating selector is done in onmove
release_data = move_data
do_event(tool, 'press', xdata=press_data[0], ydata=press_data[1], button=1)
do_event(tool, 'onmove', xdata=move_data[0], ydata=move_data[1], button=1)
assert ax.get_xbound() == x_bound
assert ax.get_ybound() == y_bound
index = 0 if direction == 'horizontal' else 1
handle_positions = [press_data[index], release_data[index]]
assert tool._edge_handles.positions == handle_positions
def test_selector_clear_method(selector):
ax = get_ax()
def onselect(*args):
pass
if selector == 'span':
tool = widgets.SpanSelector(ax,
onselect,
'horizontal',
interactive=True,
ignore_event_outside=True)
else:
tool = widgets.RectangleSelector(ax, onselect, interactive=True)
do_event(tool, 'press', xdata=10, ydata=10, button=1)
do_event(tool, 'onmove', xdata=100, ydata=120, button=1)
do_event(tool, 'release', xdata=100, ydata=120, button=1)
assert tool._selection_completed
assert tool.visible
if selector == 'span':
assert tool.extents == (10, 100)
tool.clear()
assert not tool._selection_completed
assert not tool.visible
# Do another cycle of events to make sure we can
do_event(tool, 'press', xdata=10, ydata=10, button=1)
do_event(tool, 'onmove', xdata=50, ydata=120, button=1)
do_event(tool, 'release', xdata=50, ydata=120, button=1)
assert tool._selection_completed
assert tool.visible
if selector == 'span':
assert tool.extents == (10, 50)
def check_span(*args, **kwargs):
ax = get_ax()
def onselect(vmin, vmax):
ax._got_onselect = True
assert vmin == 100
assert vmax == 150
def onmove(vmin, vmax):
assert vmin == 100
assert vmax == 125
ax._got_on_move = True
if 'onmove_callback' in kwargs:
kwargs['onmove_callback'] = onmove
tool = widgets.SpanSelector(ax, onselect, *args, **kwargs)
do_event(tool, 'press', xdata=100, ydata=100, button=1)
do_event(tool, 'onmove', xdata=125, ydata=125, button=1)
do_event(tool, 'release', xdata=150, ydata=150, button=1)
assert ax._got_onselect
if 'onmove_callback' in kwargs:
assert ax._got_on_move
def test_span_selector_direction():
ax = get_ax()
def onselect(*args):
pass
tool = widgets.SpanSelector(ax, onselect, 'horizontal', interactive=True)
assert tool.direction == 'horizontal'
assert tool._edge_handles.direction == 'horizontal'
with pytest.raises(ValueError):
tool = widgets.SpanSelector(ax, onselect, 'invalid_direction')
tool.direction = 'vertical'
assert tool.direction == 'vertical'
assert tool._edge_handles.direction == 'vertical'
with pytest.raises(ValueError):
tool.direction = 'invalid_string'
def add_widgets(self):
rect_props = dict(facecolor='blue', alpha=0.5)
self.span = mwidgets.SpanSelector(self.plot_ax,
self._on_interval_select,
'horizontal',
rectprops=rect_props,
useblit=True)
all_lr, _ = list(zip(*sorted(self.values[-1][0])))
init_lr_min = all_lr[0]
init_lr_max = self.init_lr_max or all_lr[-1]
init_wd = self.values[-1][1]
# add the plot controls
self.plot_lr_min_text: str = ''
self.plot_lr_max_text: str = ''
self.plot_lr_min_text = str(init_lr_min)
self.plot_lr_max_text = str(init_lr_max)
self.plot_wd_text = str(init_wd)
self.rerun_wd = init_wd
self.rerun_lr_min_box = mwidgets.TextBox(self.plot_lr_min_ax,
label='LR min',
initial=str(init_lr_min))
self.rerun_lr_max_box = mwidgets.TextBox(self.plot_lr_max_ax,
label='LR max',
initial=str(init_lr_max))
self.rerun_wd_box = mwidgets.TextBox(self.plot_wd_ax,
label='Weight decay',
initial=str(init_wd))
self.rerun_btn = mwidgets.Button(self.plot_btn_ax, label='PLOT')
# add event handling
self.rerun_lr_min_box.on_text_change(self._on_plot_lr_min_change)
self.rerun_lr_max_box.on_text_change(self._on_plot_lr_max_change)
self.rerun_wd_box.on_text_change(self._on_plot_wd_change)
self.rerun_btn.on_clicked(self._on_rerun_click)
# add the value input controls
self.lr_min_text = ''
self.lr_max_text = ''
self.wd_text = str(init_wd)
self.lr_min_box = mwidgets.TextBox(self.lr_min_ax, label='LR min')
self.lr_max_box = mwidgets.TextBox(self.lr_max_ax, label='LR max')
self.wd_box = mwidgets.TextBox(self.wd_ax,
label='Weight decay',
initial=str(init_wd))
self.save_btn = mwidgets.Button(self.btn_ax, label='SAVE')
# add event handling
self.rerun_lr_min_box.on_text_change(self._on_lr_min_change)
self.rerun_lr_max_box.on_text_change(self._on_lr_max_change)
self.rerun_wd_box.on_text_change(self._on_wd_change)
self.save_btn.on_clicked(self._on_save_click)
def add_zoomed_axes(self,
from_ax_nr=1,
to_ax_nr=2,
rect_props=dict(alpha=0.5, facecolor='red')):
self.ax[from_ax_nr]['span'] = widgets.SpanSelector(
self.ax[from_ax_nr][1],
lambda xmin, xmax, from_ax=from_ax_nr, to_ax=to_ax_nr: self.
_on_span_select(xmin, xmax, from_ax, to_ax),
'horizontal',
useblit=True,
rectprops=rect_props)
def subplot():
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [10, 50, 100])
span = mwidgets.SpanSelector(ax,
onselect_span,
"horizontal",
rectprops=dict(alpha=1, facecolor='red'),
span_stays=True)
# choosing span_stays = True will make the selected area stay on the image.
# lasso = mwidgets.LassoSelector(ax, onselect=onselect_lasso)
plt.show()
def Plot(filename=None, dataset=None, timemarks=None,
events=None, eventfile=None,
ylim=None, columns=1, battery="DREA160",
autoscale=True):
"""Plot from ipython.
Args:
filename (string): name of a data file to plot. This will be loaded
into a DataSet object.
dataset (DataSet): pre-existing dataset to plot. Mutually exclusive
with filename parameter.
timemarks (string): a time spec indicating a span of time to slice.
eventfile (string): name of data file containing event marks.
events (DataSet): A pre-existing event dataset.
ylim (tuple of (min, max): minimum and maximum Y values to plot.
columns (int, or sequence of ints): The column number, or numbers,
starting from zero that will be extracted out (vertical slice).
battery (string): Name of battery model. Default is "DREA160"
autoscale (bool): If True, automatically fit graph scale to data.
False means use a fixed scale (2.5 amp max).
"""
if filename is not None:
dataset = analyze.DataSet(filename=filename, timespec=timemarks)
if eventfile is not None:
events = analyze.DataSet(filename=eventfile)
if dataset is None:
print "You should supply a filename or a dataset."
return
analyze.MakeCharts(dataset, ylim=ylim, events=events,
columns=columns, autoscale=autoscale, interactive=True)
pylab.gcf().set_size_inches((9,7))
plotaxes = pylab.gca()
pylab.subplots_adjust(bottom=0.15)
capacity = analyze.BATTERIES[battery][0]
reporter = DataSampleReporter(plotaxes, dataset, capacity)
span = widgets.SpanSelector(plotaxes, reporter.StatSelected, "horizontal")
capaxes = pylab.axes([0.20, 0.025, 0.65, 0.03])
capslider = widgets.Slider(capaxes, "Batt (mA-h)", 800, 1350, capacity.inUnitsOf("mA*h").value)
capslider.on_changed(reporter.SetCapacity)
pylab.ion()
pylab.show()
def test_span_selector_add_state():
ax = get_ax()
def onselect(*args):
pass
tool = widgets.SpanSelector(ax, onselect, 'horizontal', interactive=True)
with pytest.raises(ValueError):
tool.add_state('unsupported_state')
with pytest.raises(ValueError):
tool.add_state('center')
with pytest.raises(ValueError):
tool.add_state('square')
tool.add_state('move')
def test_span_selector_onselect(interactive):
# check when press and release events take place at the same position
ax = get_ax()
def onselect(vmin, vmax):
ax._got_onselect = True
tool = widgets.SpanSelector(ax, onselect, 'horizontal',
interactive=interactive)
# move outside of axis
click_and_drag(tool, start=(100, 100), end=(150, 100))
assert tool.ax._got_onselect
assert tool.extents == (100, 150)
# Reset tool.ax._got_onselect
tool.ax._got_onselect = False
click_and_drag(tool, start=(10, 100), end=(10, 100))
assert tool.ax._got_onselect
def __init__(self, dataframe, xfield, yfield, ax=None, **kwargs):
"""
make a plot of xfield vs yfield in pandas dataframe
kwargs are passed on to pyplot.plot
"""
self.xfield = xfield
self.yfield = yfield
self.dataframe = dataframe
# create an axes if none is provided
if ax is None:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1)
self.fig = ax.figure
self.ax = ax
self.line, = ax.plot(self.dataframe[self.xfield],
self.dataframe[self.yfield], **kwargs)
# set useblit True on gtkagg for enhanced performance
import matplotlib.widgets as widgets
self.span = widgets.SpanSelector(ax,
self.on_select,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5,
facecolor='red'))
x = self.dataframe[self.xfield]
# mpl converts dates to floats runder the hood so we'll have
# to special case some stuff below if dates are involved
self._is_xdate = (isinstance(x[0], datetime.date)
or isinstance(x[0], datetime.datetime))
if self._is_xdate:
# this func rotates the x tick labels to make some room
# for long date labels
self.fig.autofmt_xdate()
self.fig.tight_layout()
def show_data(self, filename):
self.ax_data_1.clear()
self.ax_data_2.clear()
self.ax_data_2.grid()
self.df = analyse_igc(read_igc(filename))
self.df[["vs_smooth"]].plot(ax=self.ax_data_1)
self.df[["RPM"]].plot(ax=self.ax_data_2, color="green", linewidth=1)
self.span = mwidgets.SpanSelector(self.ax_data_2,
self.onselect,
'horizontal',
rectprops=dict(facecolor='blue',
alpha=0.5),
useblit=True)
# print(df[df.isna().any(axis=1)].head(50))
# plt.show()
self.fig_canvas.draw()
def test_span_selector_set_props_handle_props():
ax = get_ax()
def onselect(epress, erelease):
pass
tool = widgets.SpanSelector(ax, onselect, 'horizontal', interactive=True,
props=dict(facecolor='b', alpha=0.2),
handle_props=dict(alpha=0.5))
# Create rectangle
click_and_drag(tool, start=(0, 10), end=(100, 120))
artist = tool._selection_artist
assert artist.get_facecolor() == mcolors.to_rgba('b', alpha=0.2)
tool.set_props(facecolor='r', alpha=0.3)
assert artist.get_facecolor() == mcolors.to_rgba('r', alpha=0.3)
for artist in tool._handles_artists:
assert artist.get_color() == 'b'
assert artist.get_alpha() == 0.5
tool.set_handle_props(color='r', alpha=0.3)
for artist in tool._handles_artists:
assert artist.get_color() == 'r'
assert artist.get_alpha() == 0.3
#---- Find files within root folder according to log files ".log"
pv.FilesList = pv.FindAndOrganize_dazer(pv.Pattern_PlotFiles,
pv.Catalogue_Folder,
unpack=True,
CheckComputer=False)
#---- Generate Initial Frame
pv.FigConf('night')
pv.Axis.text(0.95,
0.05,
'Initiating visualizer',
verticalalignment='bottom',
horizontalalignment='right',
transform=pv.Axis.transAxes,
fontsize=15)
GUI = Tk_GUI(PlottingVector=pv)
pv.FigCanvas.show()
pv.FigCanvas.mpl_connect('key_press_event', Key_Manager)
Span = widgets.SpanSelector(pv.Axis,
Span_Manager,
'horizontal',
useblit=False,
rectprops=dict(alpha=1, facecolor='Blue'))
GUI.mainloop()
print "Dazer Closed"
def __init__(self, counts, positions, fig=None, pos_order=None, norm=None):
if pos_order is None:
pos_order = {"x": 0, "y": 1}
# extract x/y data
self.x_pos = xpos = positions[pos_order["x"]]
self.y_pos = ypos = positions[pos_order["y"]]
self.points = np.transpose((xpos.ravel(), ypos.ravel()))
# sort ouf the normalization
if norm is None:
norm = np.ones_like(self.x_pos)
norm = np.atleast_3d(norm[:])
self.counts = counts[:] / norm
# compute values we will use for extents below
dx = np.diff(xpos.mean(axis=0)).mean()
dy = np.diff(ypos.mean(axis=1)).mean()
left = xpos[:, 0].mean() - dx / 2
right = xpos[:, -1].mean() + dx / 2
top = ypos[0].mean() - dy / 2
bot = ypos[-1].mean() + dy / 2
# create a figure if we must
if fig is None:
import matplotlib.pyplot as plt
fig = plt.figure(tight_layout=True)
else:
# clear the figure
fig.clf()
# set the window title (look at the tool bar)
fig.canvas.set_window_title("XRF map")
self.fig = fig
# set up the figure layout
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1], figure=fig)
# set up the top panel (the map)
self.ax_im = fig.add_subplot(gs[0, 0], gid="imgmap")
self.ax_im.set_xlabel("x [?]")
self.ax_im.set_ylabel("y [?]")
self.ax_im.set_title("shift-click to select pixel\n"
"alt-drag to draw region\n"
"right-click to reset")
# set up the lower axes (the average spectrum of the ROI)
self.ax_spec = fig.add_subplot(gs[1, 0], gid="spectrum")
self.ax_spec.set_ylabel("counts [?]")
self.ax_spec.set_xlabel("bin number")
self.ax_spec.set_yscale("log")
self.ax_spec.set_title("click-and-drag to select energy ROI")
self._EROI_txt = self.ax_spec.annotate(
"ROI: all",
xy=(0, 1),
xytext=(0, 5),
xycoords="axes fraction",
textcoords="offset points",
)
self._pixel_txt = self.ax_spec.annotate(
"map average",
xy=(1, 1),
xytext=(0, -5),
xycoords="axes fraction",
textcoords="offset points",
ha="right",
va="top",
)
# show the initial image
self.im = self.ax_im.imshow(
self.counts[:, :, :].sum(axis=2),
cmap="viridis",
interpolation="nearest",
extent=[left, right, bot, top],
)
# and colorbar
self.cb = self.fig.colorbar(self.im, ax=self.ax_im)
# and the ROI mask (overlay in red)
self.mask = np.ones(self.x_pos.shape, dtype="bool")
self.mask_im = self.ax_im.imshow(
self._overlay_image,
interpolation="nearest",
extent=[left, right, bot, top],
zorder=self.im.get_zorder(),
)
self.mask_im.mouseover = False # do not consider for mouseover text
(self.overlay_plot, ) = self.ax_im.plot(
[],
[],
marker="o",
markersize=5,
markerfacecolor="none",
markeredgecolor="red",
)
# set up the spectrum, to start average everything
(self.spec, ) = self.ax_spec.plot(self.counts.mean(axis=(0, 1)), lw=2)
# set up the selector widget for the specturm
self.selector = mwidgets.SpanSelector(
self.ax_spec,
self._on_span,
"horizontal",
useblit=True,
minspan=2,
span_stays=True,
)
# placeholder for the lasso selector
self.lasso = None
# hook up the mouse events for the XRF map
self.cid = self.fig.canvas.mpl_connect("button_press_event",
self._on_click)
import matplotlib.pyplot as plt
import matplotlib.widgets as mwidgets
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [10, 50, 100])
def onselect(vmin, vmax):
print(vmin, vmax)
rectprops = dict(facecolor='blue', alpha=0.5)
span = mwidgets.SpanSelector(ax, onselect, 'horizontal', rectprops=rectprops)
fig.show()
def __init__(self,
inst,
controls=True,
xlim=None,
ylim=None,
zlim=None,
focus=None,
style='dark_background',
**kwargs):
plt.style.use(style)
self.inst = inst
self.controls = controls
self._populate_offset_dict()
self.fig = plt.figure(**kwargs)
self.ax_zx = self.fig.add_subplot(2, 2, 1)
self.ax_zy = self.fig.add_subplot(2, 2, 2)
self.ax_xy = self.fig.add_subplot(2, 2, 3)
self.ax_or = self.fig.add_subplot(2, 2, 4, projection='3d')
self._draw_labels()
self.offset = [0, 0, 0]
if self.controls:
plt.subplots_adjust(left=0.05, right=0.7)
rectprops = dict(facecolor='blue', alpha=0.5)
self.xlim_span_ax = plt.axes([0.72, 0.3, 0.25, 0.03])
self.xlim_span_ax.set_yticks([])
self.xlim_span = wid.SpanSelector(self.xlim_span_ax,
self._inst_xlim_change,
'horizontal',
rectprops=rectprops)
xlim_span_label = self._create_text("xlim",
[0.72, 0.25, 0.25, 0.03])
self.ylim_span_ax = plt.axes([0.72, 0.2, 0.25, 0.03])
self.ylim_span_ax.set_yticks([])
self.ylim_span = wid.SpanSelector(self.ylim_span_ax,
self._inst_ylim_change,
'horizontal',
rectprops=rectprops)
ylim_span_label = self._create_text("ylim",
[0.72, 0.15, 0.25, 0.03])
self.zlim_span_ax = plt.axes([0.72, 0.1, 0.25, 0.03])
self.zlim_span_ax.set_yticks([])
self.zlim_span = wid.SpanSelector(self.zlim_span_ax,
self._inst_zlim_change,
'horizontal',
rectprops=rectprops)
zlim_span_label = self._create_text("zlim",
[0.72, 0.05, 0.25, 0.03])
self.comp_focus_textbox_ax = plt.axes([0.72, 0.35, 0.25, 0.63],
facecolor='grey')
self.comp_focus_buttons = wid.RadioButtons(
self.comp_focus_textbox_ax,
tuple(kr.comp_name for kr in self.inst.kernel_refs))
self.comp_focus_buttons.on_clicked(self._inst_comp_focus)
#self.comp_focus_textbox = wid.TextBox(self.comp_focus_textbox_ax, "", color='.1', hovercolor='.15')
#self.comp_focus_textbox.on_text_change(self._inst_comp_focus)
#comp_focus_label = self._create_text("Focussed component", [0.72, 0.45, 0.25, 0.03]
if focus:
self._inst_comp_focus(focus)
if xlim:
self._inst_xlim_change(xlim[0], xlim[1])
if ylim:
self._inst_ylim_change(ylim[0], ylim[1])
if zlim:
self._inst_zlim_change(zlim[0], zlim[1])
def __init__(self, counts, positions, fig=None, pos_order=None, norm=None):
if pos_order is None:
pos_order = {'x': 0, 'y': 1}
# extract x/y data
self.x_pos = xpos = positions[pos_order['x']]
self.y_pos = ypos = positions[pos_order['y']]
self.points = np.transpose((xpos.ravel(), ypos.ravel()))
# sort ouf the normalization
if norm is None:
norm = np.ones_like(self.x_pos)
norm = np.atleast_3d(norm[:])
self.counts = counts[:] / norm
# compute values we will use for extents below
dx = np.diff(xpos.mean(axis=0)).mean()
dy = np.diff(ypos.mean(axis=1)).mean()
left = xpos[:, 0].mean() - dx / 2
right = xpos[:, -1].mean() + dx / 2
top = ypos[0].mean() - dy / 2
bot = ypos[-1].mean() + dy / 2
# create a figure if we must
if fig is None:
import matplotlib.pyplot as plt
fig = plt.figure(tight_layout=True)
# clear the figure
fig.clf()
# set the window title (look at the tool bar)
fig.canvas.set_window_title('XRF map')
self.fig = fig
# set up the figure layout
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
# set up the top panel (the map)
self.ax_im = fig.add_subplot(gs[0, 0], gid='imgmap')
self.ax_im.set_xlabel('x [?]')
self.ax_im.set_ylabel('y [?]')
self.ax_im.set_title('shift-click to select pixel, '
'alt-drag to draw region, '
'right-click to reset')
# set up the lower axes (the average spectrum of the ROI)
self.ax_spec = fig.add_subplot(gs[1, 0], gid='spectrum')
self.ax_spec.set_ylabel('counts [?]')
self.ax_spec.set_xlabel('bin number')
self.ax_spec.set_yscale('log')
self.ax_spec.set_title('click-and-drag to select energy region')
self._EROI_txt = self.ax_spec.annotate('ROI: all',
xy=(0, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points')
self._pixel_txt = self.ax_spec.annotate('map average',
xy=(1, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points',
ha='right')
# show the initial image
self.im = self.ax_im.imshow(self.counts[:, :, :].sum(axis=2),
cmap='viridis',
interpolation='nearest',
extent=[left, right, bot, top])
# and colorbar
self.cb = self.fig.colorbar(self.im, ax=self.ax_im)
# and the ROI mask (overlay in red)
self.mask = np.ones(self.x_pos.shape, dtype='bool')
self.mask_im = self.ax_im.imshow(self._overlay_image,
interpolation='nearest',
extent=[left, right, bot, top],
zorder=self.im.get_zorder())
self.mask_im.mouseover = False # do not consider for mouseover text
# set up the spectrum, to start average everything
self.spec, = self.ax_spec.plot(self.counts.mean(axis=(0, 1)), lw=2)
# set up the selector widget for the specturm
self.selector = mwidgets.SpanSelector(self.ax_spec,
self._on_span,
'horizontal',
useblit=True,
minspan=2,
span_stays=True)
# placeholder for the lasso selector
self.lasso = None
# hook up the mouse events for the XRF map
self.cid = self.fig.canvas.mpl_connect('button_press_event',
self._on_click)
def polypatch(figure, axes, line2D, verbose=False):
'''
Description:
Given a plot of Y vs X, select an interval of X and fit a polynomial to
the data.
Optionally, select a sub-interval of X to be ignored by the fit.
IMPORTANT: The code forces the fit to pass through the two last and two
first data point in the selected interval. This in order to achieve a
smoother mathing between the data and the fit. As a result, the degree
of the fit must be 3 or higher. This is done by means of Lagrange
multipliers.
The figure given as input can contain a legend, title, labels, etc.
Inputs:
figure:
It refers to the figure object from Matplotlib.
axes:
It refers to the axes object from Matplotlib.
line2D:
It refers to the line2D object from Matplotlib.
Optional inputs:
verbose:
Print additional information on the terminal. Often useful for
debugging.
Output:
The program returns a tupe where the first element is a mask which
contains the indices of the orifinal X corresponding to the fitted
interval. The second element are the new values of that interval.
Example:
import numpy as np
import matplotlib.pyplot as plt
from polypatch import polypatch
# Sample data
x = np.arange(0.25,1.50,0.01)
sigma=0.05; mu=1
gaussian = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-(x-mu)**2/(2*sigma**2) )
y = 1 + 0.5*x - x**2 + 0.5*x**3 + 0.1*gaussian
# Making a plot
figure = plt.figure()
axes = plt.axes()
axes.set_title('Example')
axes.set_ylabel('Y data')
axes.set_xlabel('X data')
line2d, = plt.plot(x,y) # Mind the comma!
# Fitting a polynomial interactively
mask, fit = polypatch(figure, axes, line2d)
# Integration of the fit values into the original data
y_modified = y.copy()
y_modified[mask] = fit
# Plot original data and modified data
figure = plt.figure()
axes = plt.axes()
axes.set_title('Example')
axes.set_ylabel('Y data')
axes.set_xlabel('X data')
plt.plot(x,y,label='original data', color='black')
plt.plot(x,y_modified,label='modified data', color='red')
plt.legend(loc='best')
plt.show()
'''
def window_info(_):
'''Display information about how to use'''
# Text separators
l1 = '--------------------------------------------------------------------------'
l2 = '=========================================================================='
# Info text
title = 'Info'
t0 = '>> To enable recognition of the left and right buttons, first hit enter <<'
t1 = '> The left button selects the interval for the fit by dragging on the plot.'
t2 = '> The right button selects an interval excluded to by excluded from the fit.'
t3 = '> Press the button [Add fit] to generate the fit.'
t4 = '> Mark the checkbox [Not refresh fit] to overplot different fits.'
t5 = '> When closing plot, you will be asked whether to save the changes.'
# Message to display
message = ('\n\n' + '\n\n'.join(
[l2, t0, l2, t1, l1, t2, l1, t3, l1, t4, l1, t5, l1]) + '\n\n')
# Create blank windows
window = tkinter.Tk()
# Create title
window.title(title)
# Add content and organize its geometry
tkinter.Label(window, text=message, justify='left').pack()
# Modify canvas and organize its geometry
tkinter.Canvas(window, width=600, height=0).pack()
# Display the window
window.mainloop()
def window_error():
'''Display information about how to use'''
title = 'Error'
text = 'Please input an integer equal or greater than 3.'
message = ('\n\n' + text + '\n\n')
window = tkinter.Tk()
window.title(title)
tkinter.Label(window, text=message, justify='center').pack()
tkinter.Canvas(window, width=600, height=0).pack()
window.mainloop()
def read_keystroke(event):
'''Get the pressed key over the axes during plot visualization'''
ivar['keystroke'] = event.key
if verbose:
print("ivar['keystroke'] = {}".format(ivar['keystroke']))
def read_button(event):
'''Get the pressed button over the axes during plot visualization'''
ivar['pressed_button'] = event.button
if verbose:
print("ivar['pressed_button']", ivar['pressed_button'])
def read_polynomial_degree(text):
'''Get the input text in the textbox during plot visualization'''
if text.isdigit() and np.int(text) > 2:
ivar['polynomial_degree'] = np.int(text)
else:
window_error()
# Keep the previous value and display it on the check box.
interface['textbox_polynomial_degree'].textbox.set_val(
ivar['polynomial_degree'])
if verbose:
print("ivar['polynomial_degree'] = {}".format(
ivar['polynomial_degree']))
def button_add_fit(event):
'''Add the fit to the plot'''
nonlocal fit_function
if fit_interval_ready:
# If a previous fit, clear it
clear_line2D(figure, lines2D['fit'], axes, redraw=False)
if not ivar['not_refresh_flag']:
clear_line2D(figure, lines2D['fit_denser'], axes, redraw=False)
# Get current colors in the axis to not repeat them
colors = [l.get_color() for l in axes.get_lines()]
# Fit
if mask['nonfit'] is not None:
if interval['xmin_nonfit'] > interval['xmin_fit'] and interval[
'xmax_nonfit'] < interval['xmax_fit']:
mask_fit_minus_nonfit = np.logical_xor(
mask['fit'], mask['nonfit'])
else:
print(
'**The non fit interval is outside the fit interval. It will be ignored.**'
)
mask_fit_minus_nonfit = mask['fit'].copy()
else:
mask_fit_minus_nonfit = mask['fit'].copy()
xfit = x[mask_fit_minus_nonfit]
yfit = y[mask_fit_minus_nonfit]
# Force the fit to match the fist two data points and the last two data points to promote a smoother match with the original
xfit_fixed = np.concatenate([[xfit[0]], [xfit[1]], [xfit[-2]],
[xfit[-1]]])
yfit_fixed = np.concatenate([[yfit[0]], [yfit[1]], [yfit[-2]],
[yfit[-1]]])
# Fit
fit_function = np.poly1d(
polyfit_with_fixed_points(ivar['polynomial_degree'], xfit,
yfit, xfit_fixed, yfit_fixed))
color = 'lime' if not 'lime' in colors else None
lines2D['fit'], = axes.plot(x[mask['fit']],
fit_function(x[mask['fit']]),
linestyle='None',
marker='o',
color=color,
markerfacecolor=color,
markeredgecolor='None')
# Plot a denser x to evidence possible wiggles between the original data
points_in_between = np.arange(0.0, 1.0, 0.1)
x_mask_diff = np.diff(x[mask['fit']])
x_mask_denser = np.array([])
for shift in points_in_between:
x_mask_denser = np.concatenate(
[x_mask_denser, x[mask['fit']][:-1] + x_mask_diff * shift])
# Add the last point
x_mask_denser = np.concatenate(
[x_mask_denser, np.array([x[mask['fit']][-1]])])
x_mask_denser = np.sort(x_mask_denser)
color = lines2D['fit'].get_color()
lines2D['fit_denser'], = axes.plot(
x_mask_denser,
fit_function(x_mask_denser),
linestyle='solid',
marker=None,
color=color,
label='Polynomial fit n={}'.format(ivar['polynomial_degree']))
list_fit_denser_line2D.append(lines2D['fit_denser'])
# Redraw
axes.legend(loc='best', ncol=1, framealpha=0.5, fontsize=10)
figure.canvas.draw()
def button_clear_fit(event):
'''Clear the fits curves from the plot'''
nonlocal list_fit_denser_line2D
nonlocal fit_function
clear_line2D(figure, lines2D['fit'], axes, redraw=True)
for line2D in list_fit_denser_line2D:
clear_line2D(figure, line2D, axes, redraw=True)
fit_function = None
def check_box_switch(label):
'''Invert the flag of not refresh'''
ivar['not_refresh_flag'] = not ivar['not_refresh_flag']
interface['checkbox'].checkbox.rectangles[0].set_fill(
ivar['not_refresh_flag'])
figure.canvas.draw()
def onselect(vmin, vmax):
'''Select the interval for the fit and the interval to be excluded in the fit'''
nonlocal fit_interval_ready
# Interval to fit
# Activate by pressing Enter and then using the left button
if ivar['keystroke'] == 'enter' and ivar['pressed_button'] == 1:
clear_line2D(figure, lines2D['fit_interval'], axes, redraw=False)
# Store the values
interval['xmin_fit'] = vmin
interval['xmax_fit'] = vmax
# Print the interval
print('Interval for the fit:')
print('xmin = {:.3},\t xmax = {:.3}\n'.format(vmin, vmax))
# Get the indices of the values within the selected span
condition1_fit = interval['xmin_fit'] < x
condition2_fit = x < interval['xmax_fit']
mask['fit'] = np.logical_and(condition1_fit, condition2_fit)
# Plot in red the selected span as an aditional Line2D object in lines
if interval['xmin_fit'] != interval['xmax_fit']:
lines2D['fit_interval'], = axes.plot(x[mask['fit']],
y[mask['fit']],
linestyle='None',
marker='o',
markerfacecolor='red',
markeredgecolor='None',
label='Fit interval')
fit_interval_ready = True
else:
fit_interval_ready = False
# Interval to exclude
# Activate by pressing Enter and then using the right button OR pressing Shift+Enter and the using left button
if (ivar['keystroke'] == 'shift+enter' and ivar['pressed_button']
== 1) or (ivar['keystroke'] == 'enter'
and ivar['pressed_button'] == 3):
clear_line2D(figure, lines2D['v1'], axes, redraw=False)
clear_line2D(figure, lines2D['v2'], axes, redraw=False)
interval['xmin_nonfit'] = vmin
interval['xmax_nonfit'] = vmax
# Print the interval
print('Interval to be excluded in the fit:')
print('xmin = {:.3},\t xmax = {:.3}\n'.format(vmin, vmax))
# Get the indices of the values within the selected span
condition1_nonfit = interval['xmin_nonfit'] < x
condition2_nonfit = x < interval['xmax_nonfit']
mask['nonfit'] = np.logical_and(condition1_nonfit,
condition2_nonfit)
# Plot in black the selected span
if interval['xmin_nonfit'] != interval['xmax_nonfit']:
lines2D['v1'] = axes.axvline(interval['xmin_nonfit'],
label='Nonfit interval',
linestyle='dashed',
color='black')
lines2D['v2'] = axes.axvline(interval['xmax_nonfit'],
linestyle='dashed',
color='black')
# Redraw
axes.legend(loc='best', ncol=1, framealpha=0.5, fontsize=10)
figure.canvas.draw()
### Initialize variables
# Curves in the plot
lines2D = {
'fit': None,
'fit_interval': None,
'fit_denser': None,
'v1': None,
'v2': None
}
# Masks
mask = {'fit': None, 'nonfit': None}
# Variable to store the fit
fit_function = None
# Interval's extrema
interval = {
'xmin_fit': None,
'xmax_fit': None,
'xmin_nonfit': None,
'xmax_nonfit': None
}
# Interactive variables
ivar = {
'keystroke': None,
'pressed_button': None,
'polynomial_degree': 3,
'not_refresh_flag': False
}
# List where to store curves of different fits
list_fit_denser_line2D = list()
# Flags
fit_interval_ready = False
# Make space for the interface of buttons
plt.subplots_adjust(bottom=0.2)
# Get the data
x = line2D.get_xdata()
y = line2D.get_ydata()
# Connect ID to the plot visualization
cid_key = figure.canvas.mpl_connect('key_press_event', read_keystroke)
cid_button = figure.canvas.mpl_connect('button_press_event', read_button)
# Biuld interface: Buttons, checkbox and textbox
height = 0.04
width = 0.1
position = [(0.65, 0.07)]
button_addfit = Button( function=button_add_fit,\
text='Add fit',\
coords=[0.65, 0.07, width, height] )
button_clearfit = Button( function=button_clear_fit,\
text='Clear fit',\
coords=[0.76, 0.07, width, height] )
button_help = Button( function=window_info,\
text='Help',\
coords=[0.76, 0.02, width, height] )
checkbox = Checkbox(function=check_box_switch,\
label='Not refresh fit',\
coords=[0.65, 0.02, width, 0.040],\
type=2)
textbox_polynomial_degree = Textbox( function=read_polynomial_degree,\
prompt_text='Polynomial degree for the fit = ',\
initial_text='{}'.format(ivar['polynomial_degree']),\
coords=[0.45, 0.05, 0.05, 0.05])
interface = {
'button_addfit': button_addfit,
'button_clearfit': button_clearfit,
'button_help': button_help,
'checkbox': checkbox,
'textbox_polynomial_degree': textbox_polynomial_degree
}
# Properties of the rectangle-span area-selector
rect_props = dict(facecolor='cyan', alpha=0.20)
# Area selector
span = mwidgets.SpanSelector(axes,
onselect,
'horizontal',
rectprops=rect_props)
# Display plot in a maximazed window
mng = plt.get_current_fig_manager()
mng.full_screen_toggle()
plt.show()
# Disconnect from the plot visuzlization
for cid in [cid_key, cid_button]:
figure.canvas.mpl_disconnect(cid)
# Print the selected intervals
print('\n')
print('=========================================')
print('Interval for the fit:')
print('xmin = {:.3}, \txmax = {:.3}'.format(interval['xmin_fit'],
interval['xmax_fit']))
print('=========================================')
print('Interval excluded from the fit:')
print('xmin = {:.3}, \txmax = {:.3}'.format(interval['xmin_nonfit'],
interval['xmax_nonfit']))
print('=========================================')
print('Polynomial degree of the fit = {}'.format(
ivar['polynomial_degree']))
print('=========================================')
print('\n')
# Return velues
if fit_function != None:
return mask['fit'], fit_function(x[mask['fit']])
else:
return None, None
def __init__(self, counts, positions, fig=None, pos_order=None,
norm=None):
if pos_order is None:
pos_order = {'x': 0,
'y': 1}
self.x_pos = xpos = positions[pos_order['x']]
self.y_pos = ypos = positions[pos_order['y']]
self.points = np.transpose((xpos.ravel(), ypos.ravel()))
if norm is None:
norm = np.ones_like(self.x_pos)
norm = np.atleast_3d(norm[:])
# TODO do not normalize up from?
self.counts = counts[:] / norm
dx = np.diff(xpos.mean(axis=0)).mean()
dy = np.diff(ypos.mean(axis=1)).mean()
left = xpos[:, 0].mean() - dx/2
right = xpos[:, -1].mean() + dx/2
top = ypos[0].mean() - dy/2
bot = ypos[-1].mean() + dy/2
if fig is None:
import matplotlib.pyplot as plt
fig = plt.figure(tight_layout=True)
fig.clf()
fig.canvas.set_window_title('XRF map')
self.fig = fig
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
self.ax_im = fig.add_subplot(gs[0, 0], gid='imgmap')
self.ax_im.set_xlabel('x [?]')
self.ax_im.set_ylabel('y [?]')
self.ax_spec = fig.add_subplot(gs[1, 0], gid='spectrum')
self.ax_spec.set_ylabel('counts [?]')
self.ax_spec.set_xlabel('bin number')
self.ax_spec.set_yscale('log')
self._EROI_txt = self.ax_spec.annotate('ROI: all',
xy=(0, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points')
self._pixel_txt = self.ax_spec.annotate('map average',
xy=(1, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points',
ha='right')
self.im = self.ax_im.imshow(self.counts[:, :, :].sum(axis=2),
cmap='viridis',
interpolation='nearest',
extent=[left, right, bot, top]
)
self.xy_plt = self.ax_im.plot(xpos.ravel(), ypos.ravel(), 'wo',
visible=False)
self.cb = self.fig.colorbar(self.im, ax=self.ax_im)
self.mask = np.ones(self.x_pos.shape, dtype='bool')
self.mask_im = self.ax_im.imshow(self._make_overlay,
interpolation='nearest',
extent=[left, right, bot, top],
zorder=self.im.get_zorder())
# do not consider for mouseover text
self.mask_im.mouseover = False
self.spec, = self.ax_spec.plot(
self.counts.mean(axis=(0, 1)),
lw=2)
self.cid = self.fig.canvas.mpl_connect('button_press_event',
self._on_click)
self.selector = mwidgets.SpanSelector(self.ax_spec,
self._on_span,
'horizontal',
useblit=True, minspan=2,
span_stays=True)
self.lasso = None
def analyze(self,
norm: bool = True,
portrait: bool = True,
blit: bool = False):
if self.freq_arr is None:
raise RuntimeError
if self.resp_arr is None:
raise RuntimeError
import matplotlib.pyplot as plt
try:
from resonator_tools import circuit
import matplotlib.widgets as mwidgets
_do_fit = True
except ImportError:
_do_fit = False
nr_amps = len(self.amp_arr)
self._AMP_IDX = nr_amps // 2
if norm:
resp_scaled = np.zeros_like(self.resp_arr)
for jj in range(nr_amps):
resp_scaled[jj] = self.resp_arr[jj] / self.amp_arr[jj]
else:
resp_scaled = self.resp_arr
resp_dB = 20. * np.log10(np.abs(resp_scaled))
amp_dBFS = 20 * np.log10(self.amp_arr / 1.0)
# choose limits for colorbar
cutoff = 1. # %
lowlim = np.percentile(resp_dB, cutoff)
highlim = np.percentile(resp_dB, 100. - cutoff)
# extent
x_min = 1e-9 * self.freq_arr[0]
x_max = 1e-9 * self.freq_arr[-1]
dx = 1e-9 * (self.freq_arr[1] - self.freq_arr[0])
y_min = amp_dBFS[0]
y_max = amp_dBFS[-1]
dy = amp_dBFS[1] - amp_dBFS[0]
if portrait:
fig1 = plt.figure(tight_layout=True, figsize=(6.4, 9.6))
ax1 = fig1.add_subplot(2, 1, 1)
# fig1 = plt.figure(tight_layout=True)
# ax1 = fig1.add_subplot(1, 1, 1)
else:
fig1 = plt.figure(tight_layout=True, figsize=(12.8, 4.8))
ax1 = fig1.add_subplot(1, 2, 1)
im = ax1.imshow(
resp_dB,
origin='lower',
aspect='auto',
interpolation='none',
extent=(x_min - dx / 2, x_max + dx / 2, y_min - dy / 2,
y_max + dy / 2),
vmin=lowlim,
vmax=highlim,
)
line_sel = ax1.axhline(amp_dBFS[self._AMP_IDX],
ls="--",
c="k",
lw=3,
animated=blit)
# ax1.set_title(f"amp = {amp_arr[AMP_IDX]:.2e}")
ax1.set_xlabel("Frequency [GHz]")
ax1.set_ylabel("Drive amplitude [dBFS]")
cb = fig1.colorbar(im)
if portrait:
cb.set_label("Response amplitude [dB]")
else:
ax1.set_title("Response amplitude [dB]")
fig1.show()
# return fig1
if portrait:
ax2 = fig1.add_subplot(4, 1, 3)
ax3 = fig1.add_subplot(4, 1, 4, sharex=ax2)
else:
ax2 = fig1.add_subplot(2, 2, 2)
ax3 = fig1.add_subplot(2, 2, 4, sharex=ax2)
ax2.yaxis.set_label_position("right")
ax2.yaxis.tick_right()
ax3.yaxis.set_label_position("right")
ax3.yaxis.tick_right()
line_a, = ax2.plot(1e-9 * self.freq_arr,
resp_dB[self._AMP_IDX],
label="measured",
animated=blit)
line_p, = ax3.plot(1e-9 * self.freq_arr,
np.angle(self.resp_arr[self._AMP_IDX]),
animated=blit)
if _do_fit:
line_fit_a, = ax2.plot(1e-9 * self.freq_arr,
np.full_like(self.freq_arr, np.nan),
ls="--",
label="fit",
animated=blit)
line_fit_p, = ax3.plot(1e-9 * self.freq_arr,
np.full_like(self.freq_arr, np.nan),
ls="--",
animated=blit)
f_min = 1e-9 * self.freq_arr.min()
f_max = 1e-9 * self.freq_arr.max()
f_rng = f_max - f_min
a_min = resp_dB.min()
a_max = resp_dB.max()
a_rng = a_max - a_min
p_min = -np.pi
p_max = np.pi
p_rng = p_max - p_min
ax2.set_xlim(f_min - 0.05 * f_rng, f_max + 0.05 * f_rng)
ax2.set_ylim(a_min - 0.05 * a_rng, a_max + 0.05 * a_rng)
ax3.set_xlim(f_min - 0.05 * f_rng, f_max + 0.05 * f_rng)
ax3.set_ylim(p_min - 0.05 * p_rng, p_max + 0.05 * p_rng)
ax3.set_xlabel("Frequency [GHz]")
ax2.set_ylabel("Response amplitude [dB]")
ax3.set_ylabel("Response phase [rad]")
ax2.legend(loc="lower right")
def onbuttonpress(event):
if event.inaxes == ax1:
self._AMP_IDX = np.argmin(np.abs(amp_dBFS - event.ydata))
update()
def onkeypress(event):
if event.inaxes == ax1:
if event.key == "up":
self._AMP_IDX += 1
if self._AMP_IDX >= len(amp_dBFS):
self._AMP_IDX = len(amp_dBFS) - 1
update()
elif event.key == "down":
self._AMP_IDX -= 1
if self._AMP_IDX < 0:
self._AMP_IDX = 0
update()
def update():
line_sel.set_ydata(
[amp_dBFS[self._AMP_IDX], amp_dBFS[self._AMP_IDX]])
# ax1.set_title(f"amp = {amp_arr[AMP_IDX]:.2e}")
print(
f"drive amp {self._AMP_IDX:d}: {self.amp_arr[self._AMP_IDX]:.2e} FS = {amp_dBFS[self._AMP_IDX]:.1f} dBFS"
)
line_a.set_ydata(resp_dB[self._AMP_IDX])
line_p.set_ydata(np.angle(self.resp_arr[self._AMP_IDX]))
if _do_fit:
line_fit_a.set_ydata(np.full_like(self.freq_arr, np.nan))
line_fit_p.set_ydata(np.full_like(self.freq_arr, np.nan))
# ax2.set_title("")
if blit:
fig1.canvas.restore_region(self._bg)
ax1.draw_artist(line_sel)
ax2.draw_artist(line_a)
ax3.draw_artist(line_p)
fig1.canvas.blit(fig1.bbox)
fig1.canvas.flush_events()
else:
fig1.canvas.draw()
if _do_fit:
def onselect(xmin, xmax):
port = circuit.notch_port(self.freq_arr,
self.resp_arr[self._AMP_IDX])
port.autofit(fcrop=(xmin * 1e9, xmax * 1e9))
if norm:
line_fit_a.set_data(
1e-9 * port.f_data, 20 * np.log10(
np.abs(port.z_data_sim /
self.amp_arr[self._AMP_IDX])))
else:
line_fit_a.set_data(1e-9 * port.f_data,
20 * np.log10(np.abs(port.z_data_sim)))
line_fit_p.set_data(1e-9 * port.f_data,
np.angle(port.z_data_sim))
# print(port.fitresults)
print("----------------")
print(f"fr = {port.fitresults['fr']}")
print(f"Qi = {port.fitresults['Qi_dia_corr']}")
print(f"Qc = {port.fitresults['Qc_dia_corr']}")
print(f"Ql = {port.fitresults['Ql']}")
print(
f"kappa = {port.fitresults['fr'] / port.fitresults['Qc_dia_corr']}"
)
print("----------------")
# ax2.set_title(
# f"fr = {1e-6*fr:.0f} MHz, Ql = {Ql:.0f}, Qi = {Qi:.0f}, Qc = {Qc:.0f}, kappa = {1e-3*kappa:.0f} kHz")
if blit:
fig1.canvas.restore_region(self._bg)
ax1.draw_artist(line_sel)
ax2.draw_artist(line_a)
ax2.draw_artist(line_fit_a)
ax3.draw_artist(line_p)
ax3.draw_artist(line_fit_p)
fig1.canvas.blit(fig1.bbox)
fig1.canvas.flush_events()
else:
fig1.canvas.draw()
rectprops = dict(facecolor='tab:gray', alpha=0.5)
fig1._span_a = mwidgets.SpanSelector(ax2,
onselect,
'horizontal',
rectprops=rectprops,
useblit=blit)
fig1._span_p = mwidgets.SpanSelector(ax3,
onselect,
'horizontal',
rectprops=rectprops,
useblit=blit)
fig1.canvas.mpl_connect('button_press_event', onbuttonpress)
fig1.canvas.mpl_connect('key_press_event', onkeypress)
fig1.show()
if blit:
fig1.canvas.draw()
fig1.canvas.flush_events()
self._bg = fig1.canvas.copy_from_bbox(fig1.bbox)
ax1.draw_artist(line_sel)
ax2.draw_artist(line_a)
ax3.draw_artist(line_p)
fig1.canvas.blit(fig1.bbox)
return fig1