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copied!<p>Just a note - I found this:</p> <p><a href="https://mail.enthought.com/pipermail/enthought-dev/2010-November/027515.html" rel="nofollow">[Enthought-Dev] is chaco faster than matplotlib</a></p> <blockquote> <p>I recall reading somewhere that you are expected to implement the _downsample method because the optimal algorithm depends on the type of data you're collecting.</p> </blockquote> <p>And as I couldn't find any examples with _downsample implementation other than <a href="http://bitbucket.org/bburan/neurobehavior/src/tip/cns/widgets/views/decimated_plot.py" rel="nofollow">decimated_plot.py</a> referred in that post, which isn't standalone - I tried and built a standalone example, included below. </p> <p>The example basically has messed up drag and zoom, (plot disappears if you go out of range, or stretches upon a drag move) - and it starts zoomed in; but it is possible to zoom it out in the range shown in the OP - and then it displays the <em>exact</em> same plot rendering problem. So downsampling isn't the solution per se, so this is likely a bug?</p> <pre><code>import numpy as np import numpy.random as npr from pprint import pprint from traits.api import HasTraits, Instance from chaco.api import Plot, ArrayPlotData, VPlotContainer from traitsui.api import View, Item from enable.component_editor import ComponentEditor from chaco.tools.api import PanTool, BetterSelectingZoom # from chaco.api import BaseXYPlot, LinearMapper, AbstractPlotData from enable.api import black_color_trait, LineStyle from traits.api import Float, Enum, Int, Str, Trait, Event, Property, Array, cached_property, Bool, Dict from chaco.abstract_mapper import AbstractMapper from chaco.abstract_data_source import AbstractDataSource from chaco.array_data_source import ArrayDataSource from chaco.data_range_1d import DataRange1D tlen = 112607 alr = npr.randint(0, 4000, tlen) tx = np.arange(0.0, 30.0-0.00001, 30.0/tlen) ty = np.arange(0, tlen, 1) % 10000 + alr pprint(len(ty)) class ChacoTest(HasTraits): container = Instance(VPlotContainer) traits_view = View( Item('container', editor=ComponentEditor(), show_label=False), width=800, height=500, resizable=True, title="Chaco Test" ) downsampling_cutoff = Int(4) def __init__(self): super(ChacoTest, self).__init__() pprint(ty) self.plotdata = ArrayPlotData(x = tx, y = ty) self.plotobj = TimeSeriesPlot(self.plotdata) self.plotobj.setplotranges("x", "y") self.container = VPlotContainer(self.plotobj, spacing=5, padding=5, bgcolor="lightgray") self.plotobj.tools.append(PanTool(self.plotobj)) self.plotobj.overlays.append(BetterSelectingZoom(self.plotobj)) # decimate from: # https://bitbucket.org/mjrosen/neurobehavior/raw/097ef3719d1263a8b303d29c31ab71b6e792ab04/cns/widgets/views/decimated_plot.py def decimate(data, screen_width, downsampling_cutoff=4, mode='extremes'): data_width = data.shape[-1] downsample = np.floor((data_width/screen_width)/4.) if downsample > downsampling_cutoff: return globals()['decimate_'+mode](data, downsample) else: return data def decimate_extremes(data, downsample): last_dim = data.ndim offset = data.shape[-1] % downsample if data.ndim == 2: shape = (len(data), -1, downsample) else: shape = (-1, downsample) data = data[..., offset:].reshape(shape).copy() data_min = data.min(last_dim) data_max = data.max(last_dim) return data_min, data_max def decimate_mean(data, downsample): offset = len(data) % downsample if data.ndim == 2: shape = (-1, downsample, data.shape[-1]) else: shape = (-1, downsample) data = data[offset:].reshape(shape).copy() return data.mean(1) # based on class from decimated_plot.py, also # neurobehavior/cns/chaco_exts/timeseries_plot.py ; # + some other code from chaco class TimeSeriesPlot(BaseXYPlot): color = black_color_trait line_width = Float(1.0) line_style = LineStyle reference = Enum('most_recent', 'trigger') traits_view = View("color@", "line_width") downsampling_cutoff = Int(100) signal_trait = "updated" decimate_mode = Str('extremes') ch_index = Trait(None, Int, None) # Mapping of data names from self.data to their respective datasources. datasources = Dict(Str, Instance(AbstractDataSource)) index_mapper = Instance(AbstractMapper) value_mapper = Instance(AbstractMapper) def __init__(self, data=None, **kwargs): super(TimeSeriesPlot, self).__init__(**kwargs) self._index_mapper_changed(None, self.index_mapper) self.setplotdata(data) self._plot_ui_info = None return def setplotdata(self, data): if data is not None: if isinstance(data, AbstractPlotData): self.data = data elif type(data) in (ndarray, tuple, list): self.data = ArrayPlotData(data) else: raise ValueError, "Don't know how to create PlotData for data" \ "of type " + str(type(data)) def setplotranges(self, index_name, value_name): self.index_name = index_name self.value_name = value_name index = self._get_or_create_datasource(index_name) value = self._get_or_create_datasource(value_name) if not(self.index_mapper): imap = LinearMapper()#(range=self.index_range) self.index_mapper = imap if not(self.value_mapper): vmap = LinearMapper()#(range=self.value_range) self.value_mapper = vmap if not(self.index_range): self.index_range = DataRange1D() # calls index_mapper if not(self.value_range): self.value_range = DataRange1D() self.index_range.add(index) # calls index_mapper! self.value_range.add(value) # now do it (right?): self.index_mapper = LinearMapper(range=self.index_range) self.value_mapper = LinearMapper(range=self.value_range) def _get_or_create_datasource(self, name): if name not in self.datasources: data = self.data.get_data(name) if type(data) in (list, tuple): data = array(data) if isinstance(data, np.ndarray): if len(data.shape) == 1: ds = ArrayDataSource(data, sort_order="none") elif len(data.shape) == 2: ds = ImageData(data=data, value_depth=1) elif len(data.shape) == 3: if data.shape[2] in (3,4): ds = ImageData(data=data, value_depth=int(data.shape[2])) else: raise ValueError("Unhandled array shape in creating new plot: " \ + str(data.shape)) elif isinstance(data, AbstractDataSource): ds = data else: raise ValueError("Couldn't create datasource for data of type " + \ str(type(data))) self.datasources[name] = ds return self.datasources[name] def get_screen_points(self): self._gather_points() return self._downsample() def _data_changed(self): self.invalidate_draw() self._cache_valid = False self._screen_cache_valid = False self.request_redraw() def _gather_points(self): if not self._cache_valid: range = self.index_mapper.range #if self.reference == 'most_recent': # values, t_lb, t_ub = self.get_recent_range(range.low, range.high) #else: # values, t_lb, t_ub = self.get_range(range.low, range.high, -1) values, t_lb, t_ub = self.data[self.value_name][range.low:range.high], range.low, range.high #if self.ch_index is None: # self._cached_data = values #else: # #self._cached_data = values[:,self.ch_index] self._cached_data = values self._cached_data_bounds = t_lb, t_ub self._cache_valid = True self._screen_cache_valid = False def _downsample(self): if not self._screen_cache_valid: val_pts = self._cached_data screen_min, screen_max = self.index_mapper.screen_bounds screen_width = screen_max-screen_min values = decimate(val_pts, screen_width, self.downsampling_cutoff, self.decimate_mode) if type(values) == type(()): n = len(values[0]) s_val_min = self.value_mapper.map_screen(values[0]) s_val_max = self.value_mapper.map_screen(values[1]) self._cached_screen_data = s_val_min, s_val_max else: s_val_pts = self.value_mapper.map_screen(values) self._cached_screen_data = s_val_pts n = len(values) t = np.linspace(*self._cached_data_bounds, num=n) t_screen = self.index_mapper.map_screen(t) self._cached_screen_index = t_screen self._screen_cache_valid = True return [self._cached_screen_index, self._cached_screen_data] def _render(self, gc, points): idx, val = points if len(idx) == 0: return gc.save_state() gc.set_antialias(True) gc.clip_to_rect(self.x, self.y, self.width, self.height) gc.set_stroke_color(self.color_) gc.set_line_width(self.line_width) #gc.set_line_width(5) gc.begin_path() #if len(val) == 2: if type(val) == type(()): starts = np.column_stack((idx, val[0])) ends = np.column_stack((idx, val[1])) gc.line_set(starts, ends) else: gc.lines(np.column_stack((idx, val))) gc.stroke_path() self._draw_default_axes(gc) gc.restore_state() if __name__ == "__main__": ChacoTest().configure_traits() </code></pre>

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