implement markers for UnivariatePlots

itten/visuals.py

@@ -3,6 +3,7 @@
 
 import matplotlib.pyplot as plt
 import matplotlib.ticker as plticker
+import numpy as np
 
 
 class SequencePlot(object):
@@ -92,7 +93,6 @@ class MultivariatePlot(SequencePlot):
         x = view[:, 0]
         y = view[:, 1]
         value = view[:, 2]
-        print('x: {0} | y: {1} | value: {2}'.format(x, y, value))
         thickness = list([int((v - view._threshold) / 4000) for v in value])
 
         self._ax, axt = self.ittenstyle(self._ax, view)
@@ -118,13 +118,26 @@ class UnivariatePlot(SequencePlot):
         self.fig = plt.figure()
         self._ax = plt.axes()
         self._ax, self._axt = self.ittenstyle(self._ax, view)
-        self.plot(view)
 
-    def plot(self, view):
+    def plot(self, view, mark_gt=False, mark_lt=False):
 
         # Interpolation mit savitzky_golay funktioniert nicht
         # contrast_points = savitzky_golay(np.array(contrast_points), 51, 7)
-        self._axt.plot(self._x, view, label=view.feature)
+        self._axt.plot(self._x, view, label=view.feature) 
+
+        # TODO Überführbar in Superclass?
+        # evtl. auch eher feature drer View Klasse
+        # TODO self._x sollte eine array sein
+        npx = np.array(self._x)
+        if mark_gt:
+            poss = npx[view > mark_gt]
+            for pos in poss:
+                self._ax.axvline(pos, color='#f3f315', alpha=0.3, linewidth=3)
+        elif mark_lt:
+            poss = npx[view > mark_gt]
+            for pos in poss:
+                self._ax.axvline(pos, color='#f3f315', alpha=0.3, linewidth=3)
+
         self._axt.legend()
 
         return self.fig

testing/bootstrap.py

@@ -3,12 +3,12 @@ from itten.views import UnivariateSequence
 from itten.visuals import UnivariatePlot
 
 movie = Movie(prefix='rec_', folder='../DHd-2017/Data/Frames/Rec/')
-# movie._frames.start = 367
-# movie._frames.end = 784
+movie._frames.start = 367
+movie._frames.end = 784
 cont = UnivariateSequence(movie._frames,)
 cont.seqmean(frm_stp=3, ctrst='saturation')
 viz = UnivariatePlot(cont)
-# viz.plot(cont)
+viz.plot(cont, mark_gt=140)
 cont.seqmad(frm_stp=3, ctrst='saturation')
 viz.plot(cont)
 viz.saveplt()