add class to visualize frame sequences with multiple features

itten/visuals.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as plticker
+
+
+class SequencePlot(object):
+    """Key class for visualizations with two axis"""
+    def __init__(self, width=40, height=3):
+        self.width = width
+        self.height = height
+
+    def _timelabels(self, val, pos):
+        min, sec = divmod(int(val), 60)
+        timelabel = "{0}:{1:02d}".format(min, sec)
+        return timelabel
+
+    # TODO styling und plotten sind hier noch etwas zusammengemischt
+    def ittenstyle(self, ax, view):
+        
+        plt.style.use('ggplot')
+        
+        fig_coef = self.width / self.height
+        tick_cnt = fig_coef / 0.3605405405
+        tick_step = int(view._frames.frm_cnt / tick_cnt)
+        
+        # loc = plticker.MultipleLocator(base=tick_freq) # this locator puts ticks at regular intervals (0.0005)
+        loc = plticker.FixedLocator(range(0, view._frames.end, tick_step))
+        fmt = plticker.FuncFormatter(self._timelabels)
+        ax.xaxis.set_major_locator(loc)
+        ax.xaxis.set_major_formatter(fmt)
+        ax.set_xlim(view._frames.start - 20, view._frames.end + 20)
+
+        # Beschriftung der Y-Achse
+        ax.set_ylim(-1, view._bins + 1)
+        loc = plticker.FixedLocator(range(0, view._bins + 1, int(view._bins / 8)))
+        ax.yaxis.set_major_locator(loc)
+        # TODO mit iter_ticks evtl noch die angegebenen Sekunden auf base 60 setzen
+
+        # obere x-achse mit zeitlich versetzten werten
+        axt = ax.twiny()
+        axt.set_xlim(ax.get_xlim())
+        # loc = plticker.MultipleLocator(base=tick_freq)
+        # loc = plticker.LinearLocator(20)
+        loc = plticker.FixedLocator(range(int(tick_step / 2), view._frames.end, tick_step))
+        fmt = plticker.FuncFormatter(self._timelabels)
+        axt.xaxis.set_major_locator(loc)
+        axt.xaxis.set_major_formatter(fmt)
+
+        ax.set_axis_bgcolor((1, 1, 1))  # TODO: gradient
+
+        chn_label = view._contrast
+        plt_title = ' Scatter Plot of the ' + chn_label + ' Channel in ' + view._frames.folder
+        ax.set_title(plt_title, {'fontsize': 14}, y=1.18)
+        ax.set_ylabel(chn_label, {'fontsize': 8})
+        ax.set_xlabel('Time', {'fontsize': 8}, y=0.5)
+        ax.yaxis.grid(False)
+        axt.yaxis.grid(False)
+        ax.tick_params(length=0)
+        axt.tick_params(length=0)
+        ax.xaxis.grid(c=(0.90, 0.90, 0.90))
+        axt.xaxis.grid(c=(0.90, 0.90, 0.90))
+
+        return (ax, axt)
+
+
+class MultivariatePlot(SequencePlot):
+    """Scatterplot that shows n features per frame"""
+    def __init__(self):
+        super(MultivariatePlot, self).__init__()
+        self.fig = plt.figure()
+        self._ax = plt.axes()
+
+    def plot(self, view):
+        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)
+
+        # Plotten
+        axt.scatter(x, y, c=(0, 0, 0), s=thickness, linewidths=0)
+        # axt.scatter(x, y, c=y, cmap='Greys_r', s=thickness, linewidths=0) # DOC: vmin/vmax sorgt für die Verteilung der Fraben der Colorm 
+        # ax.scatter(x, y, c=y, cmap='hsv', s=thickness, linewidths=0)
+
+        self.fig.tight_layout()
+        self.fig.set_size_inches(self.width, self.height) 
+
+        self.fig.savefig("intest.png", dpi=200)
+
+        return self.fig, self._ax

testing/bootstrap.py

 from itten.movie import Movie
-from itten.views import UnivariateSequence
+from itten.views import MultivariateSequence
+from itten.visuals import MultivariatePlot
 
 movie = Movie(prefix='rec_', folder='../DHd-2017/Data/Frames/Rec/')
-mean = UnivariateSequence(movie._frames)
+cont = MultivariateSequence(movie._frames)
+cont.populate(frm_stp=50) 
+viz = MultivariatePlot()
+fig, ax = viz.plot(cont)