Python FigureCanvasAgg.get_supported_filetypes方法代码示例

# 需要导入模块: from matplotlib.backends.backend_agg import FigureCanvasAgg [as 别名]
# 或者: from matplotlib.backends.backend_agg.FigureCanvasAgg import get_supported_filetypes [as 别名]
class Chart(object):
    """
    Simple and clean facade to Matplotlib's plotting API.
    
    A chart instance abstracts a plotting device, on which one or
    multiple related plots can be drawn. Charts can be exported as images, or
    visualized interactively. Each chart instance will always open in its own
    GUI window, and this window will never block the execution of the rest of
    the program, or interfere with other L{Chart}s.
    The GUI can be safely opened in the background and closed infinite number
    of times, as long as the client program is still running.
    
    By default, a chart contains a single plot:
    
    >>> chart.plot
    matplotlib.axes.AxesSubplot
    >>> chart.plot.hist(...)
    
    If C{rows} and C{columns} are defined, the chart will contain
    C{rows} x C{columns} number of plots (equivalent to MPL's sub-plots).
    Each plot can be assessed by its index:
    
    >>> chart.plots[0]
    first plot
    
    or by its position in the grid:
    
    >>> chart.plots[0, 1]
    plot at row=0, column=1
    
    @param number: chart number; by default this a L{Chart.AUTONUMBER}
    @type number: int or None
    @param title: chart master title
    @type title: str
    @param rows: number of rows in the chart window
    @type rows: int
    @param columns: number of columns in the chart window
    @type columns: int
    
    @note: additional arguments are passed directly to Matplotlib's Figure
           constructor. 
    """

    AUTONUMBER = None
    
    _serial = 0
    
    
    def __init__(self, number=None, title='', rows=1, columns=1, backend=Backends.WX_WIDGETS, *fa, **fk):
        
        if number == Chart.AUTONUMBER:
            Chart._serial += 1
            number = Chart._serial
        
        if rows < 1:
            rows = 1
        if columns < 1:
            columns = 1
            
        self._rows = int(rows)
        self._columns = int(columns)
        self._number = int(number)
        self._title = str(title)
        self._figure = Figure(*fa, **fk)
        self._figure._figure_number = self._number
        self._figure.suptitle(self._title)
        self._beclass = backend
        self._hasgui = False
        self._plots = PlotsCollection(self._figure, self._rows, self._columns)        
        self._canvas = FigureCanvasAgg(self._figure)
        
        formats = [ (f.upper(), f) for f in self._canvas.get_supported_filetypes() ]
        self._formats = csb.core.Enum.create('OutputFormats', **dict(formats))
    
    def __getitem__(self, i):
        if i in self._plots:
            return self._plots[i]
        else:
            raise KeyError('No such plot number: {0}'.format(i))
        
    def __enter__(self):
        return self
    
    def __exit__(self, *a, **k):
        self.dispose()
    
    @property
    def _backend(self):
        return Backend.get(self._beclass, started=True)

    @property
    def _backend_started(self):
        return Backend.query(self._beclass)
      
    @property
    def title(self):
        """
        Chart title
        @rtype: str
        """
#.........这里部分代码省略.........

# 需要导入模块: from matplotlib.backends.backend_agg import FigureCanvasAgg [as 别名]
# 或者: from matplotlib.backends.backend_agg.FigureCanvasAgg import get_supported_filetypes [as 别名]
class ReducePyMatplotlibHistogram: # pylint: disable=R0903 
    """
    @class ReducePyMatplotlibHistogram.PyMatplotlibHistogram is 
    a base class for classes that create histograms using matplotlib. 

    Histograms are output as JSON documents of form:

    @verbatim
    {"image": {"keywords": [...list of image keywords...],
               "description":"...a description of the image...",
               "tag": TAG,
               "image_type": "eps", 
               "data": "...base 64 encoded image..."}}
    @endverbatim

    "TAG" is specified by the sub-class. If "histogram_auto_number"
    (see below) is "true" then the TAG will have a number N appended
    where N means that the histogram was produced as a consequence of
    the (N + 1)th spill processed  by the worker. The number will be
    zero-padded to form a six digit string e.g. "00000N". If
    "histogram_auto_number" is false then no such number is appended.

    In cases where a spill is input that contains errors (e.g. is
    badly formatted or is missing the data needed to update a
    histogram) then a spill is output which is just the input spill
    with an "errors" field containing the error e.g.

    @verbatim
    {"errors": {..., "bad_json_document": "unable to do json.loads on input"}}
    {"errors": {..., "...": "..."}}
    @endverbatim

    The caller can configure the worker and specify:

    -Image type ("histogram_image_type"). Must be one of those
     supported by matplot lib (currently "svg", "ps", "emf", "rgba",
     "raw", "svgz", "pdf", "eps", "png"). Default: "eps".
    -Auto-number ("histogram_auto_number"). Default: false. Flag
     that determines if the image tag (see above) has the spill count
     appended to it or not.
    -Sub-classes may support additional configuration parameter

    Sub-classes must override:

    -_configure_at_birth - to extract any additional
     sub-class-specific configuration from data cards.
    -_update_histograms. This checks that a spill has the data
     necessary to update any histograms then creates JSON documents in
     the format described above.
    -_cleanup_at_death - to do any sub-class-specific cleanup.
    """

    def __init__(self):
        """
        Set initial attribute values.
        @param self Object reference.
        """
        # matplotlib histogram - for validation.
        figure = Figure(figsize=(6, 6))
        self.__histogram = FigureCanvas(figure)
        self.spill_count = 0 # Number of spills processed to date.
        self.image_type = "eps"
        self.auto_number = False

    def birth(self, config_json):
        """
        Configure worker from data cards. If "image_type" is not
        in those supported then a ValueError is thrown.
        @param self Object reference.
        @param config_json JSON document string.
        @returns True if configuration succeeded. 
        """
        config_doc = json.loads(config_json)

        key = "histogram_auto_number"
        if key in config_doc:
            self.auto_number = config_doc[key]

        key = "histogram_image_type"
        if key in config_doc:
            self.image_type = config_doc[key]
        else:
            self.image_type = "eps"

        if self.image_type not in \
            self.__histogram.get_supported_filetypes().keys():
            error = "Unsupported histogram image type: %s Expect one of %s" \
                % (self.image_type, 
                   self.__histogram.get_supported_filetypes().keys())
            raise ValueError(error)

        self.spill_count = 0

        # Do sub-class-specific configuration.
        return self._configure_at_birth(config_doc)

    def _configure_at_birth(self, config_doc):
        """
        Perform sub-class-specific configuration from data cards.
        Sub-classes must define this function.
#.........这里部分代码省略.........