def show(self, with_controls: bool = True, verbose: bool = False):
# graph things...
self._main_fig, self._x_axis = plt.subplots()
self._x_axis.set_xlabel('seconds')
plt.title('Shot at [%s] %.01fg, %.02f%% TDS' %
(self.shot.shot_time, self.shot.bw, self.shot.tds))
time_series = self.shot.elapsed
self._flow_plt, = plt.plot(time_series,
self.shot.flow,
label='flow (ml/s)',
color='blue',
lw=3.5)
plt.plot(time_series,
self.shot.pressure,
label='pressure (bar)',
color='green')
# derived things
if hasattr(
self, '_erroneous_weight'
): # only shown when weight curve is derived from raw scale values
plt.plot(time_series,
self._erroneous_weight,
label='error weight (g/s)',
color='brown',
alpha=0.6,
linestyle='dashed')
# tds_ratio = [(tw / w * 100) for tw, w in zip(self._tds_effect, self._smoothing(self.shot.weight, 21))]
# plt.plot(time_series, tds_ratio, label='TDS ratio (%)')
plt.plot(time_series,
self._gravimetric_water,
label='measured water (g/s)',
color='brown',
lw=2)
self._error_line, = plt.plot(time_series, [0.0] * len(time_series),
label='error (x10)',
color='grey')
error_data = self._error_series(self.shot.flow, self._stable_weight_t,
10)
self._error_line.set_ydata(
error_data) # little hack to clip large error values
self._sugg_line = plt.axhline(self._corr_suggestion * 10,
label='suggestion (x10)',
color='pink',
linestyle='dashed')
self._corr_line = plt.axhline(10.0,
label='correction (x10)',
color='magenta',
linestyle='dotted')
if verbose:
plt.plot(time_series,
self.shot.weight,
label='weight (g/s)',
color='orange',
linestyle='dashed')
resistance, = plt.plot(time_series, [0.0] * len(time_series),
label='resistance',
color='yellow')
resistance.set_ydata(self._resistance)
plt.plot(time_series, [v * 10 for v in self._diffs],
label='difference (x10)',
color='red')
plt.plot(time_series, [v * 10 for v in self._tds_effect],
label='TDS weight (g/s, x10)',
color='navy',
linestyle='dashed')
self._window_fill = self._x_axis.axvspan(*self._initial_window,
ymin=0.0,
ymax=1.0,
alpha=0.15,
color='green')
if with_controls:
# sliders
plt.subplots_adjust(right=0.85, bottom=0.18)
# correction slider
correction_ax = plt.axes([0.87, 0.18, 0.03, 0.65])
correction_slider = Slider(correction_ax,
'correction\nvalue',
orientation='vertical',
valinit=1.0,
valmin=0.3,
valmax=2.5,
valstep=0.01)
if eq_within(self.shot.current_calibration, 1.0):
corr_value_fmt = FuncFormatter(lambda v, p: 'x%.02f' % v)
else:
corr_value_fmt = FuncFormatter(
lambda v, p: 'x%.03f\n(%.02f)' %
(self.shot.current_calibration * v, v))
correction_slider._fmt = corr_value_fmt
correction_slider.on_changed(
partial(Analysis._update_flow, self, self.shot.flow))
# +- buttons
plus_button_x = plt.axes([0.92, 0.27, 0.035, 0.06])
minus_button_x = plt.axes([0.92, 0.2, 0.035, 0.06])
plus_button = Button(plus_button_x, '▲')
minus_button = Button(minus_button_x, '▼')
plus_button.on_clicked(lambda _: correction_slider.set_val(
correction_slider.val + 0.01))
minus_button.on_clicked(lambda _: correction_slider.set_val(
correction_slider.val - 0.01))
# window slider
window_ax = plt.axes([0.16, 0.05, 0.66, 0.03])
window_slider = RangeSlider(window_ax,
'opt.\nwindow',
valmin=0.0,
valmax=time_series[-1],
valstep=0.1)
window_slider.set_val(self._initial_window)
window_slider.valtext.set_visible(False)
window_slider.on_changed(partial(Analysis._update_window, self))
self._x_axis.legend()
plt.show()
