Python matplotlib 模块，lines() 实例源码

def _ts_plot(cls, ax, x, data, style=None, **kwds):
        from pandas.tseries.plotting import (_maybe_resample,
                                             _decorate_axes,
                                             format_dateaxis)
        # accept x to be consistent with normal plot func,
        # x is not passed to tsplot as it uses data.index as x coordinate
        # column_num must be in kwds for stacking purpose
        freq, data = _maybe_resample(data, ax, kwds)

        # Set ax with freq info
        _decorate_axes(ax, freq, kwds)
        # digging deeper
        if hasattr(ax, 'left_ax'):
            _decorate_axes(ax.left_ax, freq, kwds)
        if hasattr(ax, 'right_ax'):
            _decorate_axes(ax.right_ax, freq, kwds)
        ax._plot_data.append((data, cls._kind, kwds))

        lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
        # set date formatter, locators and rescale limits
        format_dateaxis(ax, ax.freq)
        return lines

def _plot(cls, ax, y, style=None, bw_method=None, ind=None,
              column_num=None, stacking_id=None, **kwds):
        from scipy.stats import gaussian_kde
        from scipy import __version__ as spv

        y = remove_na(y)

        if LooseVersion(spv) >= '0.11.0':
            gkde = gaussian_kde(y, bw_method=bw_method)
        else:
            gkde = gaussian_kde(y)
            if bw_method is not None:
                msg = ('bw_method was added in Scipy 0.11.0.' +
                       ' Scipy version in use is %s.' % spv)
                warnings.warn(msg)

        y = gkde.evaluate(ind)
        lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
        return lines

def _plot(cls, ax, y, column_num=None, return_type=None, **kwds):
        if y.ndim == 2:
            y = [remove_na(v) for v in y]
            # Boxplot fails with empty arrays, so need to add a NaN
            #   if any cols are empty
            # GH 8181
            y = [v if v.size > 0 else np.array([np.nan]) for v in y]
        else:
            y = remove_na(y)
        bp = ax.boxplot(y, **kwds)

        if return_type == 'dict':
            return bp, bp
        elif return_type == 'both':
            return cls.BP(ax=ax, lines=bp), bp
        else:
            return ax, bp

def _plot_lines(frame, x_col, y_col, ax, grp_col, colordict, cat_col, styledict):
    # plot the lines, one for each model group,
    # looking up the color by model type from above
    for grp_val in np.unique(frame[grp_col]):
        df = frame.loc[frame[grp_col] == grp_val]
        cat = df.iloc[0][cat_col]
        df.plot(x_col, y_col, ax=ax, c=colordict[cat], style=styledict[cat], legend=False)

def _draw_frame(self, iframe):
        """Draws the latest frame for each sensor on the relevant plot.
        """
        for sensor in self._plotorder:
            #First, we get the latest data point from the live feed.
            ldata = self.livefeed.read_data(sensor)
            if len(ldata) < 2: # pragma: no cover
                #We don't have anything reasonable to plot; exit gracefully.
                continue

            t = ldata[0]
            self.ts[sensor].append(t)

            for vindex in self._vindices[sensor]:
                self.ys[(sensor, vindex)].append(ldata[vindex])
                ts, ys = self.ts[sensor], self.ys[(sensor, vindex)]
                self.lines[(sensor, vindex)].set_data(ts, ys)
            if t > self.window: # pragma: no cover
                # We don't want the tests to run long enough for this window to
                # kick in (at least for the moment).
                self.axes[sensor].set_xlim((self.ts[sensor][0], t + 2.5))

            self.axes[sensor].relim() # reset intern limits of the current axes 
            self.axes[sensor].autoscale_view()   # reset axes limits 

        self._drawn_artists = self.lines.values()
        if self.testmode:
            self._timer = Timer(self.interval, self._draw_frame, (0,))
            self._timer.start()

def _init_draw(self):
        """Initializes all the subplot line objects to be empty."""
        for l in self.lines.values():
            l.set_data([], [])

def _get_marker_compat(marker):
    import matplotlib.lines as mlines
    import matplotlib as mpl
    if mpl.__version__ < '1.1.0' and marker == '.':
        return 'o'
    if marker not in mlines.lineMarkers:
        return 'o'
    return marker

def _has_plotted_object(self, ax):
        """check whether ax has data"""
        return (len(ax.lines) != 0 or
                len(ax.artists) != 0 or
                len(ax.containers) != 0)

def _make_plot(self):
        if self._is_ts_plot():
            from pandas.tseries.plotting import _maybe_convert_index
            data = _maybe_convert_index(self._get_ax(0), self.data)

            x = data.index      # dummy, not used
            plotf = self._ts_plot
            it = self._iter_data(data=data, keep_index=True)
        else:
            x = self._get_xticks(convert_period=True)
            plotf = self._plot
            it = self._iter_data()

        stacking_id = self._get_stacking_id()
        is_errorbar = any(e is not None for e in self.errors.values())

        colors = self._get_colors()
        for i, (label, y) in enumerate(it):
            ax = self._get_ax(i)
            kwds = self.kwds.copy()
            style, kwds = self._apply_style_colors(colors, kwds, i, label)

            errors = self._get_errorbars(label=label, index=i)
            kwds = dict(kwds, **errors)

            label = com.pprint_thing(label)  # .encode('utf-8')
            kwds['label'] = label

            newlines = plotf(ax, x, y, style=style, column_num=i,
                             stacking_id=stacking_id,
                             is_errorbar=is_errorbar,
                             **kwds)
            self._add_legend_handle(newlines[0], label, index=i)

            lines = _get_all_lines(ax)
            left, right = _get_xlim(lines)
            ax.set_xlim(left, right)

def _plot(cls, ax, x, y, style=None, column_num=None,
              stacking_id=None, **kwds):
        # column_num is used to get the target column from protf in line and
        # area plots
        if column_num == 0:
            cls._initialize_stacker(ax, stacking_id, len(y))
        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
        lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
        cls._update_stacker(ax, stacking_id, y)
        return lines

def _get_all_lines(ax):
    lines = ax.get_lines()

    if hasattr(ax, 'right_ax'):
        lines += ax.right_ax.get_lines()

    if hasattr(ax, 'left_ax'):
        lines += ax.left_ax.get_lines()

    return lines

def _get_xlim(lines):
    left, right = np.inf, -np.inf
    for l in lines:
        x = l.get_xdata(orig=False)
        left = min(x[0], left)
        right = max(x[-1], right)
    return left, right

def graphAllMetrics(titles, moduleMeasurements, title):
  cmap = plt.get_cmap('gnuplot')
  colors = [cmap(i) for i in np.linspace(0, 1, len(titles))] 

  for i,color in enumerate(colors, start=0):
    plt.plot(moduleMeasurements[0],moduleMeasurements[i+1],c=color,label=titles[i],linestyle='-')

  plt.xlabel('Time')
  plt.ylabel('Score')
  plt.title(title)
  # TODO:
  # Make lines instead of points for legibility
  # Add legend
  plt.legend(loc='best')
  plt.show(1)

def __init__(self, points, line, linedata):
        if isinstance(points, matplotlib.lines.Line2D):
            suffix = "pts"
        else:
            suffix = None

        self.dict_ = {"type": "linkedview",
                      "idpts": utils.get_id(points, suffix),
                      "idline": utils.get_id(line),
                      "data": linedata}

def plot_costs(case, number_of_segments=1, ax=None, legend=True):
    if ax is None:
        fig, axs = plt.subplots(1, 1, figsize=(16, 10))
        ax = axs

    color_scale = make_interpolater(0, len(case.gen_name), 0, 1)

    color = {g: plt.cm.jet(color_scale(i)) for i, g in enumerate(case.gen_name)}

    for s in calculate_segments(case, number_of_segments=number_of_segments):
        pmin, pmax = s['segment']
        x = np.linspace(pmin, pmax)
        y = x * s['slope']
        ax.plot(x, y, color=color[s['name']])

    ax = ax.twinx()
    for s in calculate_segments(case, number_of_segments=number_of_segments):
        pmin, pmax = s['segment']
        x = np.linspace(pmin, pmax)
        y = [s['slope'] for _ in x]
        ax.plot(x, y, color=color[s['name']])

    ax.set_ylim(0, 1.2*y[-1])

    if legend:
        lines = list()
        for g in case.gen_name:
            lines.append(mlines.Line2D([], [], color=color[g], label=g))
            ax.legend(handles=lines, loc='upper left')

    return ax