Python pylab.text方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def pettifor(self, xaxis=atomic_number, yaxis=atomic_number,
            label=symbol):
        """
        Create a pettifor map with the specified x- and y-axes.

        Keyword Arguments:
            xaxis, yaxis:
                Function that takes a dict as an input, and returns a number.
                Will plot this data point with the elements symbol at the
                coordinate determined by the xaxis and yaxis functions.

        Examples::

            >>> # Define a function with the desired axis label as the
            >>> # docstring and which returns the x or y coord for the point.
            >>> def eneg2(d):
            >>>     '''Electronegativity squared'''
            >>>     return d['electronegativity']**2
            >>> epd = ElementDataPlotter()
            >>> # then assign that function to an axis
            >>> # (both axes are the atomic number by default)
            >>> epd.pettifor(xaxis=eneg2)
            >>> plt.show()

        """
        x, y = [],[]
        for elt, data in self._elts.items():
            xx = xaxis(data)
            yy = yaxis(data)
            x.append(xx)
            y.append(yy)
            self._ax.text(xx, yy, label(data))
        self._ax.scatter(x, y)
        self._ax.autoscale_view()
        self._ax.set_xlabel(xaxis.__doc__)
        self._ax.set_ylabel(yaxis.__doc__) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def single_label(self, label, position="top", **kwargs):
        """
        Labels a single Patch. Label position can be set with the "position"
        keyword argument as shown below:
                
                   top
             +------------+
             |            |
             |            |
        left |     1st    | right
             |            |
             |            |
             +------------+
                bottom
        """
        assert isinstance(label, string_types)
        if position == 'top':
            x = self.x + self.dx/2
            y = self.y + self.dy*1.25
            ha, va = 'center', 'bottom'
        elif position == 'bottom':
            x = self.x + self.dx/2
            y = self.y - self.dy*0.25
            ha, va = 'center', 'top'
        elif position == 'left':
            x = self.x - self.dx*0.25
            y = self.y + self.y/2
            ha, va = 'right', 'center'
        elif position == 'right':
            x = self.x + self.dx*1.25
            y = self.y + self.y/2
            ha, va = 'left', 'center'
        else:
            raise ValueError("`position` must be one of:"
                    "'top', 'bottom', 'left', 'right'")
        fontdict = kwargs.get('fontdict', {})
        plt.text(x, y, label, ha=ha, va=va, fontdict=fontdict) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def double_label(self, labels, position="horizontal", **kwargs):
        """
        Plots 2 colors in a square:
            
                      vertical
                   +-------------+
                   |            /|
                   |   1st    /  |
                   |        /    |
        horizontal |      /      | horizontal
                   |    /        |
                   |  /   2nd    |
                   |/            |
                   +-------------+
                       vertical
        """
        assert len(labels) == 2
        if position == 'horizontal':
            x1 = self.x - self.dx*0.25
            x2 = self.x + self.dx*1.25
            y1 = y2 = self.y + self.dy/2
            ha1, ha2 = 'right', 'left'
            va1 = va2 = 'center'
        elif position == 'vertical':
            x1 = x2 = self.x + self.dx/2
            y1 = self.y + self.dy*1.25
            y2 = self.y - self.dy*0.25
            ha1 = ha2 = 'center'
            va1, va2 = 'bottom', 'top'
        else:
            raise ValueError("`position` must be one of:"
                    "'horizontal', 'vertical'")
        fontdict = kwargs.get('fontdict', {})
        plt.text(x1, y1, labels[0], ha=ha1, va=va1, fontdict=fontdict) 
        plt.text(x2, y2, labels[1], ha=ha2, va=va2, fontdict=fontdict) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def quadra_label(self, labels, **kwargs):
        """
        Plots 4 values in a square:

                +------+-----+
                |      |     |
        label1  | 1st  | 2nd | label2
                |      |     |
                +------+-----+
                |      |     |
        label4  | 4th  | 3rd | label3
                |      |     |
                +------+-----+

        """
        assert len(labels) == 4
        x1 = x4 = self.x - self.dx*0.25
        x2 = x3 = self.x + self.dx*1.25
        y1 = y2 = self.y + self.dy*0.75
        y3 = y4 = self.y + self.dy*0.25
        ha1 = ha4 = 'right'
        ha2 = ha3 = 'left'
        va = 'center'
        fontdict = kwargs.get('fontdict', {})
        plt.text(x1, y1, labels[0], ha=ha1, va=va, fontdict=fontdict)
        plt.text(x2, y2, labels[1], ha=ha2, va=va, fontdict=fontdict)
        plt.text(x3, y3, labels[2], ha=ha3, va=va, fontdict=fontdict)
        plt.text(x4, y4, labels[3], ha=ha4, va=va, fontdict=fontdict) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def plot_roc(score_list, save_dir, plot_name):

    save_path = os.path.join(save_dir, plot_name + ".jpg")
    # 按照 score 排序
    threshold_value = sorted([score for score, _ in score_list])

    threshold_num = len(threshold_value)
    accracy_array = np.zeros(threshold_num)
    precision_array = np.zeros(threshold_num)
    TPR_array = np.zeros(threshold_num)
    TNR_array = np.zeros(threshold_num)
    FNR_array = np.zeros(threshold_num)
    FPR_array = np.zeros(threshold_num)

    # calculate all the rates
    for thres in range(threshold_num):
        accracy, precision, TPR, TNR, FNR, FPR = cal_rate(score_list, threshold_value[thres])
        accracy_array[thres] = accracy
        precision_array[thres] = precision
        TPR_array[thres] = TPR
        TNR_array[thres] = TNR
        FNR_array[thres] = FNR
        FPR_array[thres] = FPR

    AUC = np.trapz(TPR_array, FPR_array)
    threshold = np.argmin(abs(FNR_array - FPR_array))
    EER = (FNR_array[threshold] + FPR_array[threshold]) / 2
    # print('EER : %f AUC : %f' % (EER, -AUC))
    plt.plot(FPR_array, TPR_array)

    plt.title('ROC')
    plt.xlabel('FPR')
    plt.ylabel('TPR')
    plt.text(0.2, 0, s="EER :{} AUC :{} Threshold:{}".format(round(EER, 4), round(-AUC, 4),
                                                             round(threshold_value[threshold], 4)), fontsize=10)
    plt.legend()
    plt.savefig(save_path)
    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def plot_confusion_matrix(y_true, y_pred, classes, figure_size=(8, 8)):
    """This function plots a confusion matrix."""
    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100

    # Build Laussen Labs colormap
    cmap = LinearSegmentedColormap.from_list('laussen_labs_green', ['w', '#43BB9B'], N=256)

    # Setup plot
    plt.figure(figsize=figure_size)

    # Plot confusion matrix
    plt.imshow(cm, interpolation='nearest', cmap=cmap)

    # Modify axes
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)
    thresh = cm.max() / 1.5
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, str(np.round(cm[i, j], 2)) + ' %', horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black", fontsize=20)
    plt.xticks(fontsize=16)
    plt.yticks(fontsize=16)
    plt.tight_layout()
    plt.ylabel('True Label', fontsize=25)
    plt.xlabel('Predicted Label', fontsize=25)

    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def make_grid(self, xelts=[], yelts=[], functions=[eneg_diff],
            cmaps='jet', draw=True, **kwargs):
        """
        Plots a grid of squares colored by one or more properties for A-B 
        element combinations. 
        """
        self.set_functions(functions, cmaps=cmaps)

        if not xelts and not yelts:
            elts = set()
            for pair in self._pairs:
                elts |= set(pair)
            xelts = list(elts)
            yelts = list(elts)

        for i, elt1 in enumerate(xelts):
            self._ax.text(i+0.5, 0.25, elt1, 
                          va='bottom', ha='center', rotation='vertical')
            self._ax.text(i+0.5, -len(yelts) - 0.25, elt1, 
                          va='top', ha='center', rotation='vertical')
            for j, elt2 in enumerate(yelts):
                pair = (elt1, elt2)
                data = self._pairs.get(pair, {})
                if isinstance(data, dict):
                    data['pair'] = pair
                if data is None:
                    continue
                vals = [ f(data) for f in self.functions ]
                square = Square(i, -j, dy=-1., data=vals, **kwargs)
                self.add_square(square)

        for j, elt2 in enumerate(yelts):
            self._ax.text(-0.25, -j-0.5, elt2, 
                          va='center', ha='right')
            self._ax.text(len(xelts) + 0.25, -j-0.5, elt2, 
                          va='center', ha='left')

        self._ax.set_xticks([])
        self._ax.set_yticks([])
        if draw:
            self.draw(**kwargs)
        self._ax.autoscale_view() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def draw(self, colorbars=True, **kwargs):
        """
        Color squares on plot appropriately, and place color bar
        
        Input:
            colorbars - boolean, Whether to print colorbars on figure
        
        Kwargs:
            font - dict, Containing information about the font for element labels
            colorbars - boolean, Whether to print colorbars
            colorbar-options - dict, Containing settings specific for the colorbars
        """
        
        # Plot each set of patches making up the squares, which correspond
        #  to the different properties being plotted
        self.cbars = []
        for coll, cmap, label in zip(self.collections, self.cmaps, self.cbar_labels):
            pc = PatchCollection(coll, cmap=cmap)
            pc.set_array(np.array([ p.value for p in coll ]))
            self._ax.add_collection(pc)

            if colorbars:
                options = {
                        'orientation':'horizontal',
                        'pad':0.05, 'aspect':60
                        }

                options.update(kwargs.get('colorbar_options', {}))
                cbar = plt.colorbar(pc, **options)
                cbar.set_label(label)
                self.cbars.append(cbar)

        # Add label to center of square
        fontdict = kwargs.get('font', {'color':'white'})
        for s in self.squares:
            if not s.label:
                continue
            x = s.x + s.dx/2
            y = s.y + s.dy/2
            self._ax.text(x, y, s.label, ha='center', 
                                         va='center', 
                                         fontdict=fontdict)

        # Plot the guide square
        if self.guide_square:
            self.guide_square.set_labels(self.labels)
            pc = PatchCollection(self.guide_square.patches, match_original=True)
            self._ax.add_collection(pc)
        self._ax.autoscale_view() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import text [as 别名]
def save_image(nifti, anat, cluster_dict, out_path, f, image_threshold=2,
               texcol=1, bgcol=0, iscale=2, text=None, **kwargs):
    '''Saves a single nifti image.

    Args:
        nifti (str or nipy.core.api.image.image.Image): nifti file to visualize.
        anat (nipy.core.api.image.image.Image): anatomical nifti file.
        cluster_dict (dict): dictionary of clusters.
        f (int): index.
        image_threshold (float): treshold for `plot_map`.
        texcol (float): text color.
        bgcol (float): background color.
        iscale (float): image scale.
        text (Optional[str]): text for figure.
        **kwargs: extra keyword arguments

    '''
    if isinstance(nifti, str):
        nifti = load_image(nifti)
        feature = nifti.get_data()
    elif isinstance(nifti, nipy.core.image.image.Image):
        feature = nifti.get_data()
    font = {'size': 8}
    rc('font', **font)

    coords = cluster_dict['top_clust']['coords']
    if coords == None:
        return

    feature /= feature.std()
    imax = np.max(np.absolute(feature))
    imin = -imax
    imshow_args = dict(
        vmax=imax,
        vmin=imin,
        alpha=0.7
    )

    coords = ([-coords[0], -coords[1], coords[2]])

    plt.axis('off')
    plt.text(0.05, 0.8, text, horizontalalignment='center',
             color=(texcol, texcol, texcol))

    try:
        plot_map(feature,
                 xyz_affine(nifti),
                 anat=anat.get_data(),
                 anat_affine=xyz_affine(anat),
                 threshold=image_threshold,
                 cut_coords=coords,
                 annotate=False,
                 cmap=cmap,
                 draw_cross=False,
                 **imshow_args)
    except Exception as e:
        return

    plt.savefig(out_path, transparent=True, facecolor=(bgcol, bgcol, bgcol))