marcel/pylot
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[prepare] starting implementation of a histogram overview plot from pick comparison dialog

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This commit is contained in:
Sebastian Wehling-Benatelli 2016-06-10 09:04:10 +02:00
parent be326cba7b
commit 280f8544e4
1 changed files with 33 additions and 1 deletions
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34
pylot/core/util/widgets.py
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@ -205,7 +205,7 @@ class ComparisonDialog(QDialog):
pdf.expectation()
_axes.plot(x, y)
_axes.set_title(phase)
_axes.set_ylabel('propability density (qual.)')
_axes.set_ylabel('propability density [-]')
_axes.set_xlabel('time difference [s]')
annotation = "{phase} difference on {station}\n" \
"expectation: {exp}\n" \
@ -217,6 +217,38 @@ class ComparisonDialog(QDialog):
_anno.set_bbox(bbox_props)
self.canvas.draw()
def plothist(self):
self.canvas = PlotWidget(self)
_axPstd, _axPexp = self.canvas.figure.add_subplot(221), self.canvas.figure.add_subplot(223)
_axSstd, _axSexp = self.canvas.figure.add_subplot(222), self.canvas.figure.add_subplot(224)
axes_dict = dict(P=dict(std=_axPstd, exp=_axPexp),
S=dict(std=_axSstd, exp=_axSexp))
bbox_props = dict(boxstyle='round', facecolor='lightgrey', alpha=.7)
for phase in self.phases:
std = self.data.get_std_array(phase)
stdxlims = [0., 1.2 * max(std)]
exp = self.data.get_expectation_array(phase)
eps_exp = 0.05 * (max(exp) - min(exp))
expxlims = [min(exp) - eps_exp, max(exp) + eps_exp]
axes_dict[phase][std].hist(std, range=stdxlims, bins=20, normed=False)
axes_dict[phase][exp].hist(std, range=expxlims, bins=20, normed=False)
std_annotation = "Distribution curve for {phase} differences'\n" \
"standard deviations (all stations)\n" \
"number of samples: {nsamples}".format(phase=phase, nsamples=len(std))
_anno_std = axes_dict[phase][std].annotate(std_annotation, xy=(.05, .8), xycoords='axes fraction')
_anno_std.set_bbox(bbox_props)
exp_annotation = "Distribution curve for {phase} differences'\n" \
"expectations (all stations)\n" \
"number of samples: {nsamples}".format(phase=phase, nsamples=len(exp))
_anno_exp = axes_dict[phase][exp].annotate(exp_annotation, xy=(.05, .8), xycoords='axes fraction')
_anno_exp.set_bbox(bbox_props)
axes_dict[phase]['exp'].set_xlabel('expectation [s]')
axes_dict[phase]['std'].set_xlabel('standard deviation [s]')
for ax in axes_dict['P'].values():
ax.set_ylabel('frequency [-]')
self.canvas.draw()
class PlotWidget(FigureCanvas):
def __init__(self, parent=None, xlabel='x', ylabel='y', title='Title'):