Python matplotlib.ticker.LogLocator() Examples

def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b) #, nbins=10)

        self.cbar_axis.set_major_locator(locator) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def _autolev(self, z, N):
        """
        Select contour levels to span the data.

        We need two more levels for filled contours than for
        line contours, because for the latter we need to specify
        the lower and upper boundary of each range. For example,
        a single contour boundary, say at z = 0, requires only
        one contour line, but two filled regions, and therefore
        three levels to provide boundaries for both regions.
        """
        if self.locator is None:
            if self.logscale:
                self.locator = ticker.LogLocator()
            else:
                self.locator = ticker.MaxNLocator(N + 1)
        zmax = self.zmax
        zmin = self.zmin
        lev = self.locator.tick_values(zmin, zmax)
        self._auto = True
        if self.filled:
            return lev
        # For line contours, drop levels outside the data range.
        return lev[(lev > zmin) & (lev < zmax)] 

def test_colorbar_renorm():
    x, y = np.ogrid[-4:4:31j, -4:4:31j]
    z = 120000*np.exp(-x**2 - y**2)

    fig, ax = plt.subplots()
    im = ax.imshow(z)
    cbar = fig.colorbar(im)

    norm = LogNorm(z.min(), z.max())
    im.set_norm(norm)
    cbar.set_norm(norm)
    cbar.locator = LogLocator()
    cbar.formatter = LogFormatter()
    cbar.update_normal(im)
    assert np.isclose(cbar.vmin, z.min())

    norm = LogNorm(z.min() * 1000, z.max() * 1000)
    im.set_norm(norm)
    cbar.set_norm(norm)
    cbar.update_normal(im)
    assert np.isclose(cbar.vmin, z.min() * 1000)
    assert np.isclose(cbar.vmax, z.max() * 1000) 

def test_colorbar_renorm():
    x, y = np.ogrid[-4:4:31j, -4:4:31j]
    z = 120000*np.exp(-x**2 - y**2)

    fig, ax = plt.subplots()
    im = ax.imshow(z)
    cbar = fig.colorbar(im)

    norm = LogNorm(z.min(), z.max())
    im.set_norm(norm)
    cbar.set_norm(norm)
    cbar.locator = LogLocator()
    cbar.formatter = LogFormatter()
    cbar.update_normal(im)
    assert np.isclose(cbar.vmin, z.min())

    norm = LogNorm(z.min() * 1000, z.max() * 1000)
    im.set_norm(norm)
    cbar.set_norm(norm)
    cbar.update_normal(im)
    assert np.isclose(cbar.vmin, z.min() * 1000)
    assert np.isclose(cbar.vmax, z.max() * 1000) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b) #, nbins=10)

        self.cbar_axis.set_major_locator(locator) 

def _autolev(self, z, N):
        """
        Select contour levels to span the data.

        We need two more levels for filled contours than for
        line contours, because for the latter we need to specify
        the lower and upper boundary of each range. For example,
        a single contour boundary, say at z = 0, requires only
        one contour line, but two filled regions, and therefore
        three levels to provide boundaries for both regions.
        """
        if self.locator is None:
            if self.logscale:
                self.locator = ticker.LogLocator()
            else:
                self.locator = ticker.MaxNLocator(N + 1)
        zmax = self.zmax
        zmin = self.zmin
        lev = self.locator.tick_values(zmin, zmax)
        self._auto = True
        if self.filled:
            return lev
        # For line contours, drop levels outside the data range.
        return lev[(lev > zmin) & (lev < zmax)] 

def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b)

        self.cbar_axis.set_major_locator(locator) 

def minorticks_on(self):
        """
        Display minor ticks on the axes.

        Displaying minor ticks may reduce performance; you may turn them off
        using `minorticks_off()` if drawing speed is a problem.
        """
        for ax in (self.xaxis, self.yaxis):
            scale = ax.get_scale()
            if scale == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            elif scale == 'symlog':
                s = ax._scale
                ax.set_minor_locator(
                    mticker.SymmetricalLogLocator(s._transform, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def _plot_walk(self, ax, parameter, data, truth=None, extents=None, convolve=None, color=None, log_scale=False):  # pragma: no cover
        if extents is not None:
            ax.set_ylim(extents)
        assert convolve is None or isinstance(convolve, int), "Convolve must be an integer pixel window width"
        x = np.arange(data.size)
        ax.set_xlim(0, x[-1])
        ax.set_ylabel(parameter)
        if color is None:
            color = "#0345A1"
        ax.scatter(x, data, c=color, s=2, marker=".", edgecolors="none", alpha=0.5)
        max_ticks = self.parent.config["max_ticks"]
        if log_scale:
            ax.set_yscale("log")
            ax.yaxis.set_major_locator(LogLocator(numticks=max_ticks))
        else:
            ax.yaxis.set_major_locator(MaxNLocator(max_ticks, prune="lower"))

        if convolve is not None:
            color2 = self.parent.color_finder.scale_colour(color, 0.5)
            filt = np.ones(convolve) / convolve
            filtered = np.convolve(data, filt, mode="same")
            ax.plot(x[:-1], filtered[:-1], ls=":", color=color2, alpha=1) 

def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b) #, nbins=10)

        self.cbar_axis.set_major_locator(locator) 

def _select_locator(self, formatter):
        '''
        select a suitable locator
        '''
        if self.boundaries is None:
            if isinstance(self.norm, colors.NoNorm):
                nv = len(self._values)
                base = 1 + int(nv/10)
                locator = ticker.IndexLocator(base=base, offset=0)
            elif isinstance(self.norm, colors.BoundaryNorm):
                b = self.norm.boundaries
                locator = ticker.FixedLocator(b, nbins=10)
            elif isinstance(self.norm, colors.LogNorm):
                locator = ticker.LogLocator()
            else:
                locator = ticker.MaxNLocator(nbins=5)
        else:
            b = self._boundaries[self._inside]
            locator = ticker.FixedLocator(b) #, nbins=10)

        self.cbar_axis.set_major_locator(locator) 

def _autolev(self, z, N):
        """
        Select contour levels to span the data.

        We need two more levels for filled contours than for
        line contours, because for the latter we need to specify
        the lower and upper boundary of each range. For example,
        a single contour boundary, say at z = 0, requires only
        one contour line, but two filled regions, and therefore
        three levels to provide boundaries for both regions.
        """
        if self.locator is None:
            if self.logscale:
                self.locator = ticker.LogLocator()
            else:
                self.locator = ticker.MaxNLocator(N + 1)
        zmax = self.zmax
        zmin = self.zmin
        lev = self.locator.tick_values(zmin, zmax)
        self._auto = True
        if self.filled:
            return lev
        # For line contours, drop levels outside the data range.
        return lev[(lev > zmin) & (lev < zmax)] 

def plot(directory, xlim, ylim, linex):
    plt.figure(figsize=(6, 6), frameon=False)

    plt.xlabel('Image size ($n$, pixels)')
    plt.xscale('log')
    plt.xlim(xlim)

    plt.ylabel('Basis-set generation time (seconds)')
    plt.yscale('log')
    plt.ylim(ylim)
    plt.gca().yaxis.set_major_locator(LogLocator(base=10.0, numticks=12))

    plt.grid(which='both', color='#EEEEEE')
    plt.grid(which='minor', linewidth=0.5)

    plt.tight_layout(pad=0.1)

    # quadratic guiding line
    plt.plot(xlim, ylim[0] * (np.array(xlim) / linex)**2,
             color='#AAAAAA', ls=':')
    # its annotation (must be done after all layout for correct rotation)
    p = plt.gca().transData.transform(np.array([[1, 1**2], [2, 2**2]]))
    plt.text(linex, ylim[0], '\n      (quadratic scaling)', color='#AAAAAA',
             va='center', linespacing=2, rotation_mode='anchor',
             rotation=90 - np.degrees(np.arctan2(*(p[1] - p[0]))))

    # all timings
    for meth, color, pargs in transforms:
        try:
            times = np.loadtxt(directory + '/' + meth + '.dat', unpack=True)
        except OSError:
            continue
        if times.shape[0] < 3:
            continue
        n = times[0]
        t = times[2] * 1e-3  # in ms
        plt.plot(n, t, 'o-', label=meth, ms=5, color=color)

    plt.legend()

    # plt.show() 

def minorticks_on(self):
        'Add autoscaling minor ticks to the axes.'
        for ax in (self.xaxis, self.yaxis):
            if ax.get_scale() == 'log':
                s = ax._scale
                ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
            else:
                ax.set_minor_locator(mticker.AutoMinorLocator()) 

def test_LogLocator():
    loc = mticker.LogLocator(numticks=5)

    assert_raises(ValueError, loc.tick_values, 0, 1000)

    test_value = np.array([1.00000000e-05, 1.00000000e-03, 1.00000000e-01,
                           1.00000000e+01, 1.00000000e+03, 1.00000000e+05,
                           1.00000000e+07, 1.000000000e+09])
    assert_almost_equal(loc.tick_values(0.001, 1.1e5), test_value)

    loc = mticker.LogLocator(base=2)
    test_value = np.array([0.5, 1., 2., 4., 8., 16., 32., 64., 128., 256.])
    assert_almost_equal(loc.tick_values(1, 100), test_value) 

def _set_axis_ticks(self, axis, ticks, log=False, rotation=0):
        """
        Allows setting the ticks for a particular axis either with
        a tuple of ticks, a tick locator object, an integer number
        of ticks, a list of tuples containing positions and labels
        or a list of positions. Also supports enabling log ticking
        if an integer number of ticks is supplied and setting a
        rotation for the ticks.
        """
        if isinstance(ticks, (list, tuple)) and all(isinstance(l, list) for l in ticks):
            axis.set_ticks(ticks[0])
            axis.set_ticklabels(ticks[1])
        elif isinstance(ticks, ticker.Locator):
            axis.set_major_locator(ticks)
        elif not ticks and ticks is not None:
            axis.set_ticks([])
        elif isinstance(ticks, int):
            if log:
                locator = ticker.LogLocator(numticks=ticks,
                                            subs=range(1,10))
            else:
                locator = ticker.MaxNLocator(ticks)
            axis.set_major_locator(locator)
        elif isinstance(ticks, (list, tuple)):
            labels = None
            if all(isinstance(t, tuple) for t in ticks):
                ticks, labels = zip(*ticks)
            axis.set_ticks(ticks)
            if labels:
                axis.set_ticklabels(labels)
        for tick in axis.get_ticklabels():
            tick.set_rotation(rotation) 

def plot_all(data_file, directory, data_range):
    print("Reading data from {}".format(directory / data_file))
    df = pd.read_csv(str(directory / data_file))
    df = df[~df.agent.isin(['agent'])].apply(pd.to_numeric, errors='ignore')
    df = df.sort_values(by="agent")

    m = df.loc[df['simple_regret'] != np.inf, 'simple_regret'].max()
    df['simple_regret'].replace(np.inf, m, inplace=True)

    df = rename_df(df)
    if data_range:
        start, end = data_range.split(':')
        df = df[df["budget"].between(int(start), int(end))]
    print("Number of seeds found: {}".format(df.seed.nunique()))

    try:
        for field in [#"total_reward", "return", "length", "mean_return",
                      "simple_regret"]:
            fig, ax = plt.subplots()
            ax.set(xscale="log")
            if field in ["simple_regret"]:
                ax.set_yscale("symlog", linthreshy=1e-2)

            sns.lineplot(x=rename("budget"), y=rename(field), ax=ax, hue="agent", data=df)
            ax.yaxis.set_minor_locator(LogLocator(base=10, subs="all"))
            ax.yaxis.grid(True, which='minor', linestyle='--')
            plt.legend(loc="lower left")

            field_path = directory / "{}.pdf".format(field)
            fig.savefig(field_path, bbox_inches='tight')
            field_path = directory / "{}.png".format(field)
            fig.savefig(field_path, bbox_inches='tight')
            print("Saving {} plot to {}".format(field, field_path))
    except ValueError as e:
        print(e)

    custom_processing(df, directory) 

def __init__(self, colorbar, *args, **kwargs):
        """
        _ColorbarLogLocator(colorbar, *args, **kwargs)

        This ticker needs to know the *colorbar* so that it can access
        its *vmin* and *vmax*.  Otherwise it is the same as
        `~.ticker.LogLocator`.  The ``*args`` and ``**kwargs`` are the
        same as `~.ticker.LogLocator`.
        """
        self._colorbar = colorbar
        super().__init__(*args, **kwargs) 

def test_sublabel(self):
        # test label locator
        fig, ax = plt.subplots()
        ax.set_xscale('log')
        ax.xaxis.set_major_locator(mticker.LogLocator(base=10, subs=[]))
        ax.xaxis.set_minor_locator(mticker.LogLocator(base=10,
                                                      subs=np.arange(2, 10)))
        ax.xaxis.set_major_formatter(mticker.LogFormatter(labelOnlyBase=True))
        ax.xaxis.set_minor_formatter(mticker.LogFormatter(labelOnlyBase=False))
        # axis range above 3 decades, only bases are labeled
        ax.set_xlim(1, 1e4)
        fmt = ax.xaxis.get_major_formatter()
        fmt.set_locs(ax.xaxis.get_majorticklocs())
        show_major_labels = [fmt(x) != ''
                             for x in ax.xaxis.get_majorticklocs()]
        assert np.all(show_major_labels)
        self._sub_labels(ax.xaxis, subs=[])

        # For the next two, if the numdec threshold in LogFormatter.set_locs
        # were 3, then the label sub would be 3 for 2-3 decades and (2,5)
        # for 1-2 decades.  With a threshold of 1, subs are not labeled.
        # axis range at 2 to 3 decades
        ax.set_xlim(1, 800)
        self._sub_labels(ax.xaxis, subs=[])

        # axis range at 1 to 2 decades
        ax.set_xlim(1, 80)
        self._sub_labels(ax.xaxis, subs=[])

        # axis range at 0.4 to 1 decades, label subs 2, 3, 4, 6
        ax.set_xlim(1, 8)
        self._sub_labels(ax.xaxis, subs=[2, 3, 4, 6])

        # axis range at 0 to 0.4 decades, label all
        ax.set_xlim(0.5, 0.9)
        self._sub_labels(ax.xaxis, subs=np.arange(2, 10, dtype=int)) 

def _ticker(self, locator, formatter):
        '''
        Return the sequence of ticks (colorbar data locations),
        ticklabels (strings), and the corresponding offset string.
        '''
        if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        if isinstance(locator, ticker.LogLocator):
            eps = 1e-10
            b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
        else:
            eps = (intv[1] - intv[0]) * 1e-10
            b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
        self._manual_tick_data_values = b
        ticks = self._locate(b)
        formatter.set_locs(b)
        ticklabels = [formatter(t, i) for i, t in enumerate(b)]
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string 

def test_basic(self):
        loc = mticker.LogLocator(numticks=5)
        with pytest.raises(ValueError):
            loc.tick_values(0, 1000)

        test_value = np.array([1.00000000e-05, 1.00000000e-03, 1.00000000e-01,
                               1.00000000e+01, 1.00000000e+03, 1.00000000e+05,
                               1.00000000e+07, 1.000000000e+09])
        assert_almost_equal(loc.tick_values(0.001, 1.1e5), test_value)

        loc = mticker.LogLocator(base=2)
        test_value = np.array([0.5, 1., 2., 4., 8., 16., 32., 64., 128., 256.])
        assert_almost_equal(loc.tick_values(1, 100), test_value) 

def test_set_params(self):
        """
        Create log locator with default value, base=10.0, subs=[1.0],
        numdecs=4, numticks=15 and change it to something else.
        See if change was successful. Should not raise exception.
        """
        loc = mticker.LogLocator()
        loc.set_params(numticks=7, numdecs=8, subs=[2.0], base=4)
        assert loc.numticks == 7
        assert loc.numdecs == 8
        assert loc._base == 4
        assert list(loc._subs) == [2.0] 

def test_majformatter_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_major_formatter(matplotlib.ticker.LogLocator()) 

def test_minformatter_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_minor_formatter(matplotlib.ticker.LogLocator()) 

def __init__(self, colorbar, *args, **kwargs):
        """
        _ColorbarLogLocator(colorbar, *args, **kwargs)

        This ticker needs to know the *colorbar* so that it can access
        its *vmin* and *vmax*.  Otherwise it is the same as
        `~.ticker.LogLocator`.  The ``*args`` and ``**kwargs`` are the
        same as `~.ticker.LogLocator`.
        """
        self._colorbar = colorbar
        super().__init__(*args, **kwargs) 

def _ticker(self, locator, formatter):
        '''
        Return the sequence of ticks (colorbar data locations),
        ticklabels (strings), and the corresponding offset string.
        '''
        if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
            intv = self._values[0], self._values[-1]
        else:
            intv = self.vmin, self.vmax
        locator.create_dummy_axis(minpos=intv[0])
        formatter.create_dummy_axis(minpos=intv[0])
        locator.set_view_interval(*intv)
        locator.set_data_interval(*intv)
        formatter.set_view_interval(*intv)
        formatter.set_data_interval(*intv)

        b = np.array(locator())
        if isinstance(locator, ticker.LogLocator):
            eps = 1e-10
            b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
        else:
            eps = (intv[1] - intv[0]) * 1e-10
            b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
        self._manual_tick_data_values = b
        ticks = self._locate(b)
        ticklabels = formatter.format_ticks(b)
        offset_string = formatter.get_offset()
        return ticks, ticklabels, offset_string