Python matplotlib.ticker() Examples

def __init__(self, base=1, month=1, day=1, tz=None):
        """
        Mark years that are multiple of base on a given month and day
        (default jan 1).
        """
        DateLocator.__init__(self, tz)
        self.base = ticker._Edge_integer(base, 0)
        self.replaced = {'month':  month,
                         'day':    day,
                         'hour':   0,
                         'minute': 0,
                         'second': 0,
                         }
        if not hasattr(tz, 'localize'):
            # if tz is pytz, we need to do this w/ the localize fcn,
            # otherwise datetime.replace works fine...
            self.replaced['tzinfo'] = tz 

def __init__(self, base=1, month=1, day=1, tz=None):
        """
        Mark years that are multiple of base on a given month and day
        (default jan 1).
        """
        DateLocator.__init__(self, tz)
        self.base = ticker._Edge_integer(base, 0)
        self.replaced = {'month':  month,
                         'day':    day,
                         'hour':   0,
                         'minute': 0,
                         'second': 0,
                         }
        if not hasattr(tz, 'localize'):
            # if tz is pytz, we need to do this w/ the localize fcn,
            # otherwise datetime.replace works fine...
            self.replaced['tzinfo'] = tz 

def addField1d(ax, field, log10plot=True,
                   xlim=None, ylim=None, scaletight=None):
        field = field.squeeze()
        assert field.dimensions == 1, 'Field needs to be 1 dimensional'
        ax.plot(field.grid, field.matrix, label=field.label)
        ax.xaxis.set_major_formatter(MatplotlibPlotter.axesformatterx)
        ax.yaxis.set_major_formatter(MatplotlibPlotter.axesformattery)
        if log10plot and ((field.matrix < 0).sum() == 0) \
                and any(field.matrix > 0):
            ax.set_yscale('log')  # sets the axis to log scale AND overrides
            # our previously set axesformatter to the default
            # matplotlib.ticker.LogFormatterMathtext.
        MatplotlibPlotter.addaxislabels(ax, field)
        ax.autoscale(tight=scaletight)
        if xlim is not None:
            ax.set_xlim(xlim)
        if ylim is not None:
            ax.set_ylim(ylim)
        return ax 

def __init__(self, base=1, month=1, day=1, tz=None):
        """
        Mark years that are multiple of base on a given month and day
        (default jan 1).
        """
        DateLocator.__init__(self, tz)
        self.base = ticker._Edge_integer(base, 0)
        self.replaced = {'month':  month,
                         'day':    day,
                         'hour':   0,
                         'minute': 0,
                         'second': 0,
                         }
        if not hasattr(tz, 'localize'):
            # if tz is pytz, we need to do this w/ the localize fcn,
            # otherwise datetime.replace works fine...
            self.replaced['tzinfo'] = tz 

def set_plot(amp, function):
    global figure_w, figure_h, fig
    fig=plt.figure()
    ax = fig.add_subplot(111)
    x = np.linspace(-np.pi*2, np.pi*2, 100)
    if function == 'sine':
        y= amp*np.sin(x)
        ax.set_title('sin(x)')
    else:
        y=amp*np.cos(x)
        ax.set_title('cos(x)')
    plt.plot(x/np.pi,y)

    
    #centre bottom and left axes to zero

    ax.spines['left'].set_position('zero')
    ax.spines['right'].set_color('none')
    ax.spines['bottom'].set_position('zero')
    ax.spines['top'].set_color('none')

    #Format axes - nicer eh!
    ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%g $\pi$'))

    figure_x, figure_y, figure_w, figure_h = fig.bbox.bounds 

def set_plot(amp, function):
    global figure_w, figure_h, fig
    fig=plt.figure()
    ax = fig.add_subplot(111)
    x = np.linspace(-np.pi*2, np.pi*2, 100)
    if function == 'sine':
        y= amp*np.sin(x)
        ax.set_title('sin(x)')
    else:
        y=amp*np.cos(x)
        ax.set_title('cos(x)')
    plt.plot(x/np.pi,y)

    
    #centre bottom and left axes to zero

    ax.spines['left'].set_position('zero')
    ax.spines['right'].set_color('none')
    ax.spines['bottom'].set_position('zero')
    ax.spines['top'].set_color('none')

    #Format axes - nicer eh!
    ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%g $\pi$'))

    figure_x, figure_y, figure_w, figure_h = fig.bbox.bounds 

def mask_ques(sen, attn, idx2word):
		"""
		Put attention weights to each word in sentence.
		--------------------
		Arguments:
			sen (LongTensor): encoded sentence.
			attn (FloatTensor): attention weights of each word.
			idx2word (dict): vocabulary.
		"""
		fig, ax = plt.subplots(figsize=(15,15))
		ax.matshow(attn, cmap='bone')
		y = [1]
		x = [1] + [idx2word[i] for i in sen]
		ax.set_yticklabels(y)
		ax.set_xticklabels(x)
		ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
		ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) 

def twinx(self):
        """
        Create a twin Axes sharing the xaxis

        Create a new Axes instance with an invisible x-axis and an independent
        y-axis positioned opposite to the original one (i.e. at right). The
        x-axis autoscale setting will be inherited from the original Axes.
        To ensure that the tick marks of both y-axes align, see
        `~matplotlib.ticker.LinearLocator`

        Returns
        -------
        ax_twin : Axes
            The newly created Axes instance

        Notes
        -----
        For those who are 'picking' artists while using twinx, pick
        events are only called for the artists in the top-most axes.
        """
        ax2 = self._make_twin_axes(sharex=self)
        ax2.yaxis.tick_right()
        ax2.yaxis.set_label_position('right')
        ax2.yaxis.set_offset_position('right')
        ax2.set_autoscalex_on(self.get_autoscalex_on())
        self.yaxis.tick_left()
        ax2.xaxis.set_visible(False)
        ax2.patch.set_visible(False)
        return ax2 

def __init__(self, interval=1, tz=None):
        """
        *interval* is the interval between each iteration.  For
        example, if ``interval=2``, mark every second microsecond.

        """
        self._interval = interval
        self._wrapped_locator = ticker.MultipleLocator(interval)
        self.tz = tz 

def __init__(self, base=1, month=1, day=1, tz=None):
        """
        Mark years that are multiple of base on a given month and day
        (default jan 1).
        """
        DateLocator.__init__(self, tz)
        self.base = ticker._Edge_integer(base, 0)
        self.replaced = {'month':  month,
                         'day':    day,
                         'hour':   0,
                         'minute': 0,
                         'second': 0,
                         'tzinfo': tz
                         } 

def twiny(self):
        """
        Create a twin Axes sharing the yaxis

        Create a new Axes instance with an invisible y-axis and an independent
        x-axis positioned opposite to the original one (i.e. at top). The
        y-axis autoscale setting will be inherited from the original Axes.
        To ensure that the tick marks of both x-axes align, see
        `~matplotlib.ticker.LinearLocator`

        Returns
        -------
        ax_twin : Axes
            The newly created Axes instance

        Notes
        -----
        For those who are 'picking' artists while using twiny, pick
        events are only called for the artists in the top-most axes.
        """

        ax2 = self._make_twin_axes(sharey=self)
        ax2.xaxis.tick_top()
        ax2.xaxis.set_label_position('top')
        ax2.set_autoscaley_on(self.get_autoscaley_on())
        self.xaxis.tick_bottom()
        ax2.yaxis.set_visible(False)
        ax2.patch.set_visible(False)
        return ax2 

def view_limits(self, vmin, vmax):
        """
        Try to choose the view limits intelligently.

        """
        b = self._transform.base
        if vmax < vmin:
            vmin, vmax = vmax, vmin

        if not matplotlib.ticker.is_decade(abs(vmin), b):
            if vmin < 0:
                vmin = -_base_up(-vmin, b)
            else:
                vmin = _base_down(vmin, b)
        if not matplotlib.ticker.is_decade(abs(vmax), b):
            if vmax < 0:
                vmax = -_base_down(-vmax, b)
            else:
                vmax = _base_up(vmax, b)

        if vmin == vmax:
            if vmin < 0:
                vmin = -_base_up(-vmin, b)
                vmax = -_base_down(-vmax, b)
            else:
                vmin = _base_down(vmin, b)
                vmax = _base_up(vmax, b)
        result = matplotlib.transforms.nonsingular(vmin, vmax)
        return result 

def set_default_locators_and_formatters(self, axis):
        """
        Set up the locators and formatters for the scale.

        Parameters
        ----------
        axis: matplotlib.axis
            Axis for which to set locators and formatters.

        """
        axis.set_major_locator(_LogicleLocator(self._transform))
        axis.set_minor_locator(_LogicleLocator(self._transform,
                                               subs=np.arange(2.0, 10.)))
        axis.set_major_formatter(matplotlib.ticker.LogFormatterSciNotation(
            labelOnlyBase=True)) 

def _cat_format(dimension, x, _):
    """Categorical axis tick formatter function.  Returns the name of category
    `x` in `dimension`.  Used with `matplotlib.ticker.FuncFormatter`."""
    return str(dimension.categories[int(x)]) 

def util_plot2d(metric, title='', subtitle = '', line_size=2, title_size=17,date_formatter='%b-%d-%y %H-%M',plot_size=(6,6)):
    if not isinstance(metric, list):
        sys.exit("metric should have class 'list'")
    if not isinstance(title, str):
        sys.exit("title should have class 'str'")
    if not isinstance(subtitle, str):
        sys.exit("subtitle should have class 'str'")
    times=util_timezone(metric[0])

    def format_date(x, pos=None):
        thisind = np.clip(int(x+0.5), 0, N-1)
        return times[thisind].strftime(date_formatter)
    N = len(metric[1])
    ind = np.arange(N)  # the evenly spaced plot indices
    fig, ax = plt.subplots()
    ax.plot(ind, metric[1], lw=line_size)
    ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
    fig.autofmt_xdate()

    
    plt.suptitle(title, y=0.99, fontsize=title_size)
    plt.title(subtitle, fontsize=title_size-5)
    plt.rcParams['figure.figsize'] = plot_size

    plt.grid(True) 
    plt.show() 

def showAttention(path, s, c, attentions, index):
    # Set up figure with colorbar
    fig = plt.figure()
    ax = fig.add_subplot(111)
    cax = ax.matshow(attentions.numpy(), cmap='bone')
    fig.colorbar(cax)
    # Set up axes
    ax.set_xticklabels([''] + s, rotation=90)
    ax.set_yticklabels([''] + c)
    # Show label at every tick
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
    plt.show()
    plt.savefig(path + str(index) + '.jpg') 

def locator_params(self, axis='both', tight=None, **kwargs):
        """
        Control behavior of tick locators.

        Parameters
        ----------
        axis : {'both', 'x', 'y'}, optional
            The axis on which to operate.

        tight : bool or None, optional
            Parameter passed to :meth:`autoscale_view`.
            Default is None, for no change.

        Other Parameters
        ----------------
        **kw :
            Remaining keyword arguments are passed to directly to the
            :meth:`~matplotlib.ticker.MaxNLocator.set_params` method.

        Typically one might want to reduce the maximum number
        of ticks and use tight bounds when plotting small
        subplots, for example::

            ax.locator_params(tight=True, nbins=4)

        Because the locator is involved in autoscaling,
        :meth:`autoscale_view` is called automatically after
        the parameters are changed.

        This presently works only for the
        :class:`~matplotlib.ticker.MaxNLocator` used
        by default on linear axes, but it may be generalized.
        """
        _x = axis in ['x', 'both']
        _y = axis in ['y', 'both']
        if _x:
            self.xaxis.get_major_locator().set_params(**kwargs)
        if _y:
            self.yaxis.get_major_locator().set_params(**kwargs)
        self.autoscale_view(tight=tight, scalex=_x, scaley=_y) 

def locator_params(self, axis='both', tight=None, **kwargs):
        """
        Control behavior of tick locators.

        Keyword arguments:

        *axis*
            ['x' | 'y' | 'both']  Axis on which to operate;
            default is 'both'.

        *tight*
            [True | False | None] Parameter passed to :meth:`autoscale_view`.
            Default is None, for no change.

        Remaining keyword arguments are passed to directly to the
        :meth:`~matplotlib.ticker.MaxNLocator.set_params` method.

        Typically one might want to reduce the maximum number
        of ticks and use tight bounds when plotting small
        subplots, for example::

            ax.locator_params(tight=True, nbins=4)

        Because the locator is involved in autoscaling,
        :meth:`autoscale_view` is called automatically after
        the parameters are changed.

        This presently works only for the
        :class:`~matplotlib.ticker.MaxNLocator` used
        by default on linear axes, but it may be generalized.
        """
        _x = axis in ['x', 'both']
        _y = axis in ['y', 'both']
        if _x:
            self.xaxis.get_major_locator().set_params(**kwargs)
        if _y:
            self.yaxis.get_major_locator().set_params(**kwargs)
        self.autoscale_view(tight=tight, scalex=_x, scaley=_y) 

def twiny(self):
        """
        Create a twin Axes sharing the yaxis

        Create a new Axes instance with an invisible y-axis and an independent
        x-axis positioned opposite to the original one (i.e. at top). The
        y-axis autoscale setting will be inherited from the original Axes.
        To ensure that the tick marks of both x-axes align, see
        `~matplotlib.ticker.LinearLocator`

        Returns
        -------
        ax_twin : Axes
            The newly created Axes instance

        Notes
        -----
        For those who are 'picking' artists while using twiny, pick
        events are only called for the artists in the top-most axes.
        """

        ax2 = self._make_twin_axes(sharey=self)
        ax2.xaxis.tick_top()
        ax2.xaxis.set_label_position('top')
        ax2.set_autoscaley_on(self.get_autoscaley_on())
        self.xaxis.tick_bottom()
        ax2.yaxis.set_visible(False)
        ax2.patch.set_visible(False)
        return ax2 

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 test_minlocator_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_minor_locator(matplotlib.ticker.LogFormatter()) 

def __init__(self, interval=1, tz=None):
        """
        *interval* is the interval between each iteration.  For
        example, if ``interval=2``, mark every second microsecond.

        """
        self._interval = interval
        self._wrapped_locator = ticker.MultipleLocator(interval)
        self.tz = tz 

def __init__(self, interval=1, tz=None):
        """
        *interval* is the interval between each iteration.  For
        example, if ``interval=2``, mark every second microsecond.

        """
        self._interval = interval
        self._wrapped_locator = ticker.MultipleLocator(interval)
        self.tz = tz 

def visualize_confusion(writer, step, matrix, class_dict, save_path):
    """
    Visualization of confusion matrix. Is saved to hard-drive and TensorBoard.

    Parameters:
        writer (tensorboard.SummaryWriter): TensorBoard SummaryWriter instance.
        step (int): Counter usually specifying steps/epochs/time.
        matrix (numpy.array): Square-shaped array of size class x class.
            Should specify cross-class accuracies/confusion in percent
            values (range 0-1).
        class_dict (dict): Dictionary specifying class names as keys and
            corresponding integer labels/targets as values.
        save_path (str): Path used for saving
    """

    all_categories = sorted(class_dict, key=class_dict.get)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    cax = ax.matshow(matrix)
    fig.colorbar(cax, boundaries=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])

    # Set up axes
    ax.set_xticklabels([''] + all_categories, rotation=90)
    ax.set_yticklabels([''] + all_categories)

    # Force label at every tick
    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

    # Turn off the grid for this plot
    ax.grid(False)
    plt.tight_layout()

    writer.add_figure("Training data", fig, global_step=str(step))
    plt.savefig(os.path.join(save_path, 'confusion_epoch_' + str(step) + '.png'), bbox_inches='tight') 

def locator_params(self, axis='both', tight=None, **kwargs):
        """
        Control behavior of tick locators.

        Parameters
        ----------
        axis : ['both' | 'x' | 'y'], optional
            The axis on which to operate.

        tight : bool or None, optional
            Parameter passed to :meth:`autoscale_view`.
            Default is None, for no change.

        Other Parameters
        ----------------
        **kw :
            Remaining keyword arguments are passed to directly to the
            :meth:`~matplotlib.ticker.MaxNLocator.set_params` method.

        Typically one might want to reduce the maximum number
        of ticks and use tight bounds when plotting small
        subplots, for example::

            ax.locator_params(tight=True, nbins=4)

        Because the locator is involved in autoscaling,
        :meth:`autoscale_view` is called automatically after
        the parameters are changed.

        This presently works only for the
        :class:`~matplotlib.ticker.MaxNLocator` used
        by default on linear axes, but it may be generalized.
        """
        _x = axis in ['x', 'both']
        _y = axis in ['y', 'both']
        if _x:
            self.xaxis.get_major_locator().set_params(**kwargs)
        if _y:
            self.yaxis.get_major_locator().set_params(**kwargs)
        self.autoscale_view(tight=tight, scalex=_x, scaley=_y) 

def twinx(self):
        """
        Create a twin Axes sharing the xaxis

        Create a new Axes instance with an invisible x-axis and an independent
        y-axis positioned opposite to the original one (i.e. at right). The
        x-axis autoscale setting will be inherited from the original Axes.
        To ensure that the tick marks of both y-axes align, see
        `~matplotlib.ticker.LinearLocator`

        Returns
        -------
        ax_twin : Axes
            The newly created Axes instance

        Notes
        -----
        For those who are 'picking' artists while using twinx, pick
        events are only called for the artists in the top-most axes.
        """
        ax2 = self._make_twin_axes(sharex=self)
        ax2.yaxis.tick_right()
        ax2.yaxis.set_label_position('right')
        ax2.yaxis.set_offset_position('right')
        ax2.set_autoscalex_on(self.get_autoscalex_on())
        self.yaxis.tick_left()
        ax2.xaxis.set_visible(False)
        ax2.patch.set_visible(False)
        return ax2 

def twiny(self):
        """
        Create a twin Axes sharing the yaxis

        Create a new Axes instance with an invisible y-axis and an independent
        x-axis positioned opposite to the original one (i.e. at top). The
        y-axis autoscale setting will be inherited from the original Axes.
        To ensure that the tick marks of both x-axes align, see
        `~matplotlib.ticker.LinearLocator`

        Returns
        -------
        ax_twin : Axes
            The newly created Axes instance

        Notes
        -----
        For those who are 'picking' artists while using twiny, pick
        events are only called for the artists in the top-most axes.
        """

        ax2 = self._make_twin_axes(sharey=self)
        ax2.xaxis.tick_top()
        ax2.xaxis.set_label_position('top')
        ax2.set_autoscaley_on(self.get_autoscaley_on())
        self.xaxis.tick_bottom()
        ax2.yaxis.set_visible(False)
        ax2.patch.set_visible(False)
        return ax2 

def __init__(self, base=1, month=1, day=1, tz=None):
        """
        Mark years that are multiple of base on a given month and day
        (default jan 1).
        """
        DateLocator.__init__(self, tz)
        self.base = ticker.Base(base)
        self.replaced = {'month':  month,
                         'day':    day,
                         'hour':   0,
                         'minute': 0,
                         'second': 0,
                         'tzinfo': tz
                         } 

def __init__(self, interval=1, tz=None):
        """
        *interval* is the interval between each iteration.  For
        example, if ``interval=2``, mark every second microsecond.

        """
        self._interval = interval
        self._wrapped_locator = ticker.MultipleLocator(interval)
        self.tz = tz