Python matplotlib.gridspec方法代码示例

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def evaluate(epoch):

    samples = generator(fixed_z).cpu().data.numpy()[:64]


    fig = plt.figure(figsize=(8, 8))
    gs = gridspec.GridSpec(8, 8)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(samples):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(sample.transpose((1,2,0)) * 0.5 + 0.5)

    if not os.path.exists('out/'):
        os.makedirs('out/')

    plt.savefig('out/{}.png'.format(str(epoch).zfill(3)), bbox_inches='tight')
    plt.close(fig) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def __init__(self, *args, **kwargs):
    super(NuPICPlotOutput, self).__init__(*args, **kwargs)
    self.names = [self.name]
    # Turn matplotlib interactive mode on.
    plt.ion()
    self.dates = []
    self.convertedDates = []
    self.actualValues = []
    self.predictedValues = []
    self.actualLines = []
    self.predictedLines = []
    self.linesInitialized = False
    self.graphs = []
    plotCount = len(self.names)
    plotHeight = max(plotCount * 3, 6)
    fig = plt.figure(figsize=(14, plotHeight))
    gs = gridspec.GridSpec(plotCount, 1)
    for index in range(len(self.names)):
      self.graphs.append(fig.add_subplot(gs[index, 0]))
      plt.title(self.names[index])
      plt.ylabel('Frequency Bucket')
      plt.xlabel('Seconds')
    plt.tight_layout() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def show_jigsaw(input_batch, perm, name):
    import matplotlib.gridspec as gridspec
    fig = plt.figure(figsize=(6, 6))
    outer = gridspec.GridSpec(3, 3)
    outer.update(wspace=0.05, hspace=0.05)
    for i in range(9):
        img = input_batch[i, :, :, :].copy()
        img[0,0,0] = 1.0 
        img[0,1,0] = 0.0 
        ax = plt.subplot(outer[int(perm[i]/3),perm[i]%3])
        ax.axis('off')
        ax.get_xaxis().set_visible(False) # this removes the ticks and numbers for x axis
        ax.get_yaxis().set_visible(False) # this removes the ticks and numbers for y axis
        ax.imshow( np.squeeze(img) )
    fig.savefig(name, dpi=128, bbox_inches='tight', pad_inches=0.0)
    plt.close() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def plot_images(args, x_sample, dir, file_name, size_x=3, size_y=3):

    fig = plt.figure(figsize=(size_x, size_y))
    # fig = plt.figure(1)
    gs = gridspec.GridSpec(size_x, size_y)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(x_sample):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        sample = sample.reshape((args.input_size[0], args.input_size[1], args.input_size[2]))
        sample = sample.swapaxes(0, 2)
        sample = sample.swapaxes(0, 1)
        if (args.input_type == 'binary') or (args.input_type in ['multinomial'] and args.input_size[0] == 1):
            sample = sample[:, :, 0]
            plt.imshow(sample, cmap='gray', vmin=0, vmax=1)
        else:
            plt.imshow(sample)

    plt.savefig(dir + file_name + '.png', bbox_inches='tight')
    plt.close(fig) 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def axpos(self, axpos):
        if axpos is None:
            self._axpos = axpos

            return

        if isinstance(axpos, list) or isinstance(axpos, np.ndarray):
            axpos = tuple(axpos)

        if isinstance(axpos, tuple) and (len(axpos) == 3 or len(axpos) == 6) and np.all(isinstance(ap, int) for ap in axpos):
            self._axpos = axpos

        elif isinstance(axpos, int) and axpos >= 100 and axpos < 1000:
            self._axpos = (int(axpos/100), int(axpos/10 % 10), int(axpos % 10))

        elif isinstance(axpos, int) and axpos >= 110011 and axpos < 999999:
            self._axpos = tuple([int(ap) for ap in str(axpos)])

        else:
            raise ValueError("axpos must be of type int or tuple between 100 and 999 (subplot syntax: ncols, nrows, ind) or 110011 and 999999 (gridspec syntax: ncols, nrows, indx, indy, widthx, widthy)") 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def _tile_vertical(imgs, glimpses, boxes, n_objects, fig_size, img_size, colors):
    # prepare figure
    yy, xx = imgs.shape[0], 1 + n_objects
    fig_y, fig_x = fig_size
    img_y, img_x = img_size

    sy, sx = yy * img_y, n_objects + img_x
    gs = gridspec.GridSpec(sy, sx)
    fig = plt.figure(figsize=(sx * fig_x, sy * fig_y))

    axes = np.empty((yy, xx), dtype=object)
    ii = 0
    for i in xrange(yy):
        axes[i, 0] = plt.subplot(gs[i * img_y:(i + 1) * img_y, :img_x])

    for i in xrange(yy):
        for j in xrange(1, xx):
            axes[i, j] = plt.subplot(gs[i * img_y:(i + 1) * img_y, j + img_x - 1])

    # plot
    for r in xrange(yy):
        axes[r, 0].imshow(imgs[r], 'gray')

        for n in xrange(n_objects):
            for (k, v), color in izip(boxes.iteritems(), colors):
                y, x, h, w = boxes[k]
                bbox = Rectangle((x[r, n], y[r, n]), w[r, n], h[r, n],
                                 edgecolor=color, facecolor='none', label=k)
                axes[r, 0].add_patch(bbox)

        for c in xrange(1, xx):
            axes[r, c].imshow(glimpses[r, c - 1], 'gray')

    # TODO: improve
    len_bbox = len(boxes)
    if len_bbox > 1:
        x_offset = .25 * len_bbox
        axes[-1, 0].legend(bbox_to_anchor=(x_offset, -.75),
                           ncol=len_bbox, loc='lower center')

    return fig, axes 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def __init__(self, gridspec, num1, num2=None):
        """
        The subplot will occupy the num1-th cell of the given
        gridspec.  If num2 is provided, the subplot will span between
        num1-th cell and num2-th cell.

        The index stars from 0.
        """

        rows, cols = gridspec.get_geometry()
        total = rows*cols

        self._gridspec = gridspec
        self.num1 = num1
        self.num2 = num2 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def get_position(self, fig, return_all=False):
        """
        update the subplot position from fig.subplotpars
        """

        gridspec = self.get_gridspec()
        nrows, ncols = gridspec.get_geometry()

        figBottoms, figTops, figLefts, figRights = \
                    gridspec.get_grid_positions(fig)


        rowNum, colNum =  divmod(self.num1, ncols)
        figBottom = figBottoms[rowNum]
        figTop = figTops[rowNum]
        figLeft = figLefts[colNum]
        figRight = figRights[colNum]

        if self.num2 is not None:

            rowNum2, colNum2 =  divmod(self.num2, ncols)
            figBottom2 = figBottoms[rowNum2]
            figTop2 = figTops[rowNum2]
            figLeft2 = figLefts[colNum2]
            figRight2 = figRights[colNum2]

            figBottom = min(figBottom, figBottom2)
            figLeft = min(figLeft, figLeft2)
            figTop = max(figTop, figTop2)
            figRight = max(figRight, figRight2)

        figbox = mtransforms.Bbox.from_extents(figLeft, figBottom,
                                               figRight, figTop)


        if return_all:
            return figbox, rowNum, colNum, nrows, ncols
        else:
            return figbox 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def get_topmost_subplotspec(self):
        'get the topmost SubplotSpec instance associated with the subplot'
        gridspec = self.get_gridspec()
        if hasattr(gridspec, "get_topmost_subplotspec"):
            return gridspec.get_topmost_subplotspec()
        else:
            return self 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def subplot2grid(shape, loc, rowspan=1, colspan=1, **kwargs):
    """
    Create a subplot in a grid.  The grid is specified by *shape*, at
    location of *loc*, spanning *rowspan*, *colspan* cells in each
    direction.  The index for loc is 0-based. ::

      subplot2grid(shape, loc, rowspan=1, colspan=1)

    is identical to ::

      gridspec=GridSpec(shape[0], shape[2])
      subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)
      subplot(subplotspec)
    """

    fig = gcf()
    s1, s2 = shape
    subplotspec = GridSpec(s1, s2).new_subplotspec(loc,
                                                   rowspan=rowspan,
                                                   colspan=colspan)
    a = fig.add_subplot(subplotspec, **kwargs)
    bbox = a.bbox
    byebye = []
    for other in fig.axes:
        if other==a: continue
        if bbox.fully_overlaps(other.bbox):
            byebye.append(other)
    for ax in byebye: delaxes(ax)

    draw_if_interactive()
    return a 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def __init__(self, *args, **kwargs):
        super(NuPICPlotOutput, self).__init__(*args, **kwargs)
        # Turn matplotlib interactive mode on.
        plt.ion()
        self.dates = []
        self.convertedDates = []
        self.value = []
        self.rawValue = []
        self.allValues = []
        self.allRawValues = []
        self.predicted = []
        self.anomalyScore = []
        self.anomalyLikelihood = []
        self.actualLine = None
        self.rawLine = None
        self.predictedLine = None
        self.anomalyScoreLine = None
        self.anomalyLikelihoodLine = None
        self.linesInitialized = False
        self._chartHighlights = []
        fig = plt.figure(figsize=(16, 10))
        gs = gridspec.GridSpec(2, 1, height_ratios=[3,    1])

        self._mainGraph = fig.add_subplot(gs[0, 0])
        plt.title(self.name)
        plt.ylabel('Value')
        plt.xlabel('Date')

        self._anomalyGraph = fig.add_subplot(gs[1])

        plt.ylabel('Percentage')
        plt.xlabel('Date')

        # Maximizes window
        mng = plt.get_current_fig_manager()
        mng.resize(800, 600)

        plt.tight_layout() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def __init__(self, name, bins, maximize, anomaly_threshold, anomaly_trigger_count):
    self.name = name
    self.bins = bins
    self.anomaly_threshold = anomaly_threshold
    self.anomaly_trigger_count = anomaly_trigger_count
    # Turn matplotlib interactive mode on.
    plt.ion()
    self.seconds = []
    self.bin_values = {}
    self.anomaly_likelihoods = {}
    self.bin_lines = {}
    self.anomaly_likelihood_lines = {}
    self.lines_initialized = False
    self._chart_highlights = []
    fig = plt.figure(figsize=(16, 10))
    gs = gridspec.GridSpec(2, 1, height_ratios=[3,  1])

    self._mainGraph = fig.add_subplot(gs[0, 0])
    plt.title(self.name)
    plt.xlabel('Seconds')

    self._anomalyGraph = fig.add_subplot(gs[1])

    plt.ylabel('Anomalies')
    plt.xlabel('Seconds')

    # Maximizes window
    if maximize:
      mng = plt.get_current_fig_manager()
      mng.resize(*mng.window.maxsize())

    plt.tight_layout() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def create_gridspec(bams, transcript_file, annotation_files, sv_type, read_data):
    """Helper function for creation of a correctly-sized GridSpec instance
    """
    # give one axis to display each sample
    num_ax = len(bams)

    # add another if we are displaying the SV
    if sv_type:
        num_ax += 1

    # add another if a annotation file is given
    if transcript_file:
        num_ax += 1

    if annotation_files:
        num_ax += len(annotation_files)

    # set the relative sizes for each
    ratios = []
    if sv_type:
        ratios = [1]

    for i in range(len(bams)):
        ratios.append(len(read_data["all_coverages"][i]) * 3)
        if len(read_data["all_coverages"]) > 0:
            ratios[-1] = 9

    if annotation_files:
        ratios += [0.3] * len(annotation_files)
    if transcript_file:
        ratios.append(2)

    return gridspec.GridSpec(num_ax, 1, height_ratios=ratios), num_ax


# }}}

##Annotations/Transcript methods
# {{{def get_plot_annotation_plan(ranges, annotation_file): 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def show_blocks(directory):
        """!
        @brief Show BANG-blocks (leafs only) in data space.
        @details BANG-blocks represents grid that was used for clustering process.

        @param[in] directory (bang_directory): Directory that was created by BANG algorithm during clustering process.

        """

        dimension = len(directory.get_data()[0])

        amount_canvases = 1
        if dimension > 1:
            amount_canvases = int(dimension * (dimension - 1) / 2)

        figure = plt.figure()
        grid_spec = gridspec.GridSpec(1, amount_canvases)

        pairs = list(itertools.combinations(range(dimension), 2))
        if len(pairs) == 0: pairs = [(0, 0)]

        for index in range(amount_canvases):
            ax = figure.add_subplot(grid_spec[index])
            bang_visualizer.__draw_blocks(ax, directory.get_leafs(), pairs[index])
            bang_visualizer.__draw_two_dimension_data(ax, directory.get_data(), pairs[index])

        plt.show() 

# 需要导入模块: import matplotlib [as 别名]
# 或者: from matplotlib import gridspec [as 别名]
def show_grid(cells, data):
        """!
        @brief Show CLIQUE blocks as a grid in data space.
        @details Each block contains points and according to this density is displayed. CLIQUE grid helps to visualize
                  grid that was used for clustering process.

        @param[in] cells (list): List of cells that is produced by CLIQUE algorithm.
        @param[in] data (array_like): Input data that was used for clustering process.

        """
        dimension = cells[0].dimensions

        amount_canvases = 1
        if dimension > 1:
            amount_canvases = int(dimension * (dimension - 1) / 2)

        figure = plt.figure()
        grid_spec = gridspec.GridSpec(1, amount_canvases)

        pairs = list(itertools.combinations(range(dimension), 2))
        if len(pairs) == 0: pairs = [(0, 0)]

        for index in range(amount_canvases):
            ax = figure.add_subplot(grid_spec[index])
            clique_visualizer.__draw_cells(ax, cells, pairs[index])
            clique_visualizer.__draw_two_dimension_data(ax, data, pairs[index])

        plt.show()