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ReducePyMatplotlibHistogram is a base class for classes that create
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histograms using matplotlib.
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# This file is part of MAUS: http://micewww.pp.rl.ac.uk:8080/projects/maus
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# MAUS is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# MAUS is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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# You should have received a copy of the GNU General Public License
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# along with MAUS. If not, see <http://www.gnu.org/licenses/>.
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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from matplotlib.figure import Figure
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class ReducePyMatplotlibHistogram: # pylint: disable=R0903
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@class ReducePyMatplotlibHistogram.PyMatplotlibHistogram is
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a base class for classes that create histograms using matplotlib.
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PyMatplotlibHistogram maintains a histogram created using
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The histogram output is a JSON document of form:
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{"image": {"content":"...a description of the image...",
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"data": "...base 64 encoded image..."}}
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where "TAG" is specified by the sub-class. If
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"histogram_auto_number" (see below) is "true" then the TAG will
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have a number N appended where N means that the histogram was
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produced as a consequence of the (N + 1)th spill processed
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by the worker. The number will be zero-padded to form a six digt
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string e.g. "00000N". If "histogram_auto_number" is false
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then no such number is appended.
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In case of errors the output document is just the input document
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with an "errors" field containing the error e.g.
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{"errors": {..., "bad_json_document": "unable to do json.loads on input"}}
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{"errors": {..., "no_digits": "no digits"}}
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The caller can configure the worker and specify:
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-Image type ("histogram_image_type"). Must be one of those
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supported by matplot lib (currently "svg", "ps", "emf", "rgba",
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"raw", "svgz", "pdf", "eps", "png"). Default: "eps".
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-Auto-number ("histogram_auto_number"). Default: false. Flag
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that determines if the image tag (see above) has the spill count
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appended to it or not.
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Sub-classes must override:
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-_configure_at_birth - to extract any additional
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sub-class-specific configuration from data cards.
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-_update_histogram. This should update the histogram, axes, scale
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and make any other desired changes. The _content attribute can
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be updated with a textual description of the content of the
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-_cleanup_at_death - to do any sub-class-specific cleanup.
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Set initial attribute values.
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@param self Object reference.
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self.spill_count = 0 # Number of spills processed to date.
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self.image_type = "eps"
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self.auto_number = False
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self._histogram = None # matplotlib histogram.
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self._tag = "graph" # Histogram name tag.
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self._content = "" # Description of histogram content.
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def birth(self, config_json):
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Configure worker from data cards. If "image_type" is not
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in those supported then a ValueError is thrown.
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@param self Object reference.
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@param config_json JSON document string.
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@returns True if configuration succeeded.
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config_doc = json.loads(config_json)
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self._histogram = self._create_histogram()
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key = "histogram_auto_number"
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if key in config_doc:
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self.auto_number = config_doc[key]
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key = "histogram_image_type"
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if key in config_doc:
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self.image_type = config_doc[key]
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self.image_type = "eps"
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if self.image_type not in \
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self._histogram.get_supported_filetypes().keys():
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error = "Unsupported histogram image type: %s Expect one of %s" \
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self._histogram.get_supported_filetypes().keys())
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raise ValueError(error)
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# Do sub-class-specific configuration.
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return self._configure_at_birth(config_doc)
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def _configure_at_birth(self, config_doc):
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Perform sub-class-specific configuration from data cards. When
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this is called a sub-class can assume that self._histogram
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points to a matplotlib FigureCanvas that they can then customise.
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@param self Object reference.
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@param config_json JSON document.
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@returns True if configuration succeeded.
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def process(self, json_string):
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Update the histogram with data from the current spill
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and output the histogram.
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@param self Object reference.
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@param json_string String with current JSON document.
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@returns JSON document containing current histogram.
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# Load and validate the JSON document.
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json_doc = json.loads(json_string.rstrip())
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json_doc = {"errors": {"bad_json_document":
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"unable to do json.loads on input"} }
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return json.dumps(json_doc)
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result = self._update_histogram(json_doc)
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return json.dumps(result)
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return self._create_image_json()
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def _update_histogram(self, json_doc):
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Update the histogram with data from the current spill
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and output the histogram. Sub-classes must define this.
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@param self Object reference.
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@param json_doc Current spill..
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@returns None if histogram was created or a JSON document with
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error messages if there were problems (e.g. information was
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missing from the spill).
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def death(self): #pylint: disable=R0201
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Invokes _cleanup_at_death().
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return self._cleanup_at_death()
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def _cleanup_at_death(self):
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A no-op. Can be overridden by sub-classes for sub-class-specific
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clean-up at death time.
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@param self Object reference.
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def _create_histogram(self): #pylint: disable=R0201
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Create a histogram using matplotlib.
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@param self Object reference.
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@returns matplotlib FigureCanvas representing the histogram.
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figure = Figure(figsize=(6, 6))
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histogram = FigureCanvas(figure)
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axes = figure.add_subplot(111)
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axes.grid(True, linestyle="-", color="0.75")
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def _rescale_axes(self, xmin, xmax, ymin, ymax, xfudge = 0.5, yfudge = 0.5): #pylint: disable=C0301, R0913
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Rescale the X and Y axes of the histogram to show the given
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axis ranges. Fudge factors are used avoid matplotlib warning
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about "Attempting to set identical bottom==top" which arises
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if the axes are set to be exactly the maximum of the data.
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@param self Object reference.
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@param xmin Minimum X value.
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@param xmax Maximum X value.
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@param ymin Minimum Y value.
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@param ymin Maximum Y value.
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@param xfudge X fudge factor.
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@param yfudge Y fudge factor.
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@returns matplotlib FigureCanvas representing the histogram.
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# Fudge factors are used
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self._histogram.figure.get_axes()[0].set_xlim( \
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[xmin, xmax + xfudge])
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self._histogram.figure.get_axes()[0].set_ylim( \
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[ymin, ymax + yfudge])
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def _create_image_json(self):
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Create JSON document for output by the worker with the
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content description, image type, tag and base-64 encoded
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data from the histogram.
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@param self Object reference.
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@returns JSON document containing current histogram.
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json_doc["image"] = {}
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if (self.auto_number):
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tag = "%s%06d" % (self._tag, self.spill_count)
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tag = "%s" % (self._tag)
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data = self.__convert_to_binary(self._histogram)
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json_doc["image"]["content"] = self._content
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json_doc["image"]["tag"] = tag
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json_doc["image"]["image_type"] = self.image_type
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json_doc["image"]["data"] = data
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self.spill_count += 1
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return json.dumps(json_doc)
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def __convert_to_binary(self, histogram): #pylint: disable=R0201
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Convert histogram to binary format.
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@param self Object reference.
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@param histogram matplotlib FigureCanvas representing the
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@returns representation of histogram in base 64-encoded image
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data_file = StringIO.StringIO()
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histogram.print_figure(data_file, dpi=500, format=self.image_type)
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data = data_file.read()
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return base64.b64encode(data)
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src/reduce/ReducePyMatplotlibHistogram/ReducePyMatplotlibHistogram.py
