Python matplotlib.ticker方法代碼示例

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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)) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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() 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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
                         } 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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
                         } 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
def test_majformatter_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_major_formatter(matplotlib.ticker.LogLocator()) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
def plot_scores_histogram_log(thresholds, all_counts, xlabel, true_counts=None, ax=None):
    plt.figure()
    # First graph
    if ax is None:
        _, ax = plt.subplots()
    width = (thresholds[1] - thresholds[0]) / 2
    offset = [i + width for i in thresholds]
    if true_counts is not None:
        falses = [i - j for i, j in zip(all_counts, true_counts)]
        plt.bar(offset, falses, width=width,
                log=True, label="False items")
        plt.bar(thresholds, true_counts, width=width,
                log=True, color="purple", label="True items")
    else:
        plt.bar(thresholds, all_counts, width=width,
                log=True, color="purple", label="All items")
    plt.grid(False)
    ax.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
    ax.yaxis.get_major_formatter().set_scientific(False)
    # Write to image
    image_data = StringIO()
    plt.xlim((0.0, 1.0))
    plt.xlabel(xlabel)
    plt.legend(loc='best')
    plt.savefig(image_data, format='svg')
    image_data.seek(0)
    return image_data 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
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) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
def test_minformatter_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_minor_formatter(matplotlib.ticker.LogLocator()) 

# 需要導入模塊: import matplotlib [as 別名]
# 或者: from matplotlib import ticker [as 別名]
def test_minlocator_type():
    fig, ax = plt.subplots()
    with pytest.raises(TypeError):
        ax.xaxis.set_minor_locator(matplotlib.ticker.LogFormatter())