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gerrit-public.fairphone.software / platform / external / autotest / c5fcff6e13beb99165930240c1f008a07974b136 / . / frontend / tko / graphing_utils.py
blob: 9d1a9fb035de0f90c2eab77f51be66c3c0511a11 [file] [log] [blame]
Aviv Keshet2d218a02013-06-21 15:05:30 -0700[diff] [blame]1# pylint: disable-msg=C0111
2
3import base64, os, tempfile, pickle, datetime, django.db
showard90e27262009-06-08 23:25:19 +0000[diff] [blame]4import os.path, getpass
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]5from math import sqrt
6
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]7# When you import matplotlib, it tries to write some temp files for better
showard90e27262009-06-08 23:25:19 +0000[diff] [blame]8# performance, and it does that to the directory in MPLCONFIGDIR, or, if that
9# doesn't exist, the home directory. Problem is, the home directory is not
10# writable when running under Apache, and matplotlib's not smart enough to
11# handle that. It does appear smart enough to handle the files going
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]12# away after they are written, though.
showard90e27262009-06-08 23:25:19 +0000[diff] [blame]13
14temp_dir = os.path.join(tempfile.gettempdir(),
15 '.matplotlib-%s' % getpass.getuser())
16if not os.path.exists(temp_dir):
17 os.mkdir(temp_dir)
18os.environ['MPLCONFIGDIR'] = temp_dir
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]19
Aviv Keshet2d218a02013-06-21 15:05:30 -0700[diff] [blame]20try:
21 import matplotlib
22 matplotlib.use('Agg')
23 import matplotlib.figure, matplotlib.backends.backend_agg
24 import StringIO, colorsys, PIL.Image, PIL.ImageChops
25except ImportError:
26 # Do nothing, in case this is part of a unit test, so the unit test
27 # can proceed.
28 pass
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]29
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]30from autotest_lib.frontend.afe import readonly_connection
31from autotest_lib.frontend.afe.model_logic import ValidationError
Alex Miller4f341702013-03-25 12:39:12 -0700[diff] [blame]32from json import encoder
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]33from autotest_lib.client.common_lib import global_config
showard250d84d2010-01-12 21:59:48 +0000[diff] [blame]34from autotest_lib.frontend.tko import models, tko_rpc_utils
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]35
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]36_FIGURE_DPI = 100
37_FIGURE_WIDTH_IN = 10
38_FIGURE_BOTTOM_PADDING_IN = 2 # for x-axis labels
39
40_SINGLE_PLOT_HEIGHT = 6
41_MULTIPLE_PLOT_HEIGHT_PER_PLOT = 4
42
43_MULTIPLE_PLOT_MARKER_TYPE = 'o'
44_MULTIPLE_PLOT_MARKER_SIZE = 4
45_SINGLE_PLOT_STYLE = 'bs-' # blue squares with lines connecting
46_SINGLE_PLOT_ERROR_BAR_COLOR = 'r'
47
48_LEGEND_FONT_SIZE = 'xx-small'
49_LEGEND_HANDLE_LENGTH = 0.03
50_LEGEND_NUM_POINTS = 3
51_LEGEND_MARKER_TYPE = 'o'
52
53_LINE_XTICK_LABELS_SIZE = 'x-small'
54_BAR_XTICK_LABELS_SIZE = 8
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]55
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]56_json_encoder = encoder.JSONEncoder()
57
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]58class NoDataError(Exception):
59 """\
60 Exception to raise if the graphing query returned an empty resultset.
61 """
62
63
64def _colors(n):
65 """\
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]66 Generator function for creating n colors. The return value is a tuple
67 representing the RGB of the color.
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]68 """
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]69 for i in xrange(n):
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]70 yield colorsys.hsv_to_rgb(float(i) / n, 1.0, 1.0)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]71
72
73def _resort(kernel_labels, list_to_sort):
74 """\
75 Resorts a list, using a list of kernel strings as the keys. Returns the
76 resorted list.
77 """
78
79 labels = [tko_rpc_utils.KernelString(label) for label in kernel_labels]
80 resorted_pairs = sorted(zip(labels, list_to_sort))
81
82 # We only want the resorted list; we are not interested in the kernel
83 # strings.
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]84 return [pair[1] for pair in resorted_pairs]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]85
86
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]87def _quote(string):
88 return "%s%s%s" % ("'", string.replace("'", r"\'"), "'")
89
90
91_HTML_TEMPLATE = """\
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]92<html><head></head><body>
93<img src="data:image/png;base64,%s" usemap="#%s"
94 border="0" alt="graph">
95<map name="%s">%s</map>
96</body></html>"""
97
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]98_AREA_TEMPLATE = """\
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]99<area shape="rect" coords="%i,%i,%i,%i" title="%s"
100href="#"
101onclick="%s(%s); return false;">"""
102
103
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]104class MetricsPlot(object):
105 def __init__(self, query_dict, plot_type, inverted_series, normalize_to,
106 drilldown_callback):
107 """
108 query_dict: dictionary containing the main query and the drilldown
109 queries. The main query returns a row for each x value. The first
110 column contains the x-axis label. Subsequent columns contain data
111 for each series, named by the column names. A column named
112 'errors-<x>' will be interpreted as errors for the series named <x>.
113
114 plot_type: 'Line' or 'Bar', depending on the plot type the user wants
115
116 inverted_series: list of series that should be plotted on an inverted
117 y-axis
118
119 normalize_to:
120 None - do not normalize
121 'first' - normalize against the first data point
122 'x__%s' - normalize against the x-axis value %s
123 'series__%s' - normalize against the series %s
124
125 drilldown_callback: name of drilldown callback method.
126 """
127 self.query_dict = query_dict
128 if plot_type == 'Line':
129 self.is_line = True
130 elif plot_type == 'Bar':
131 self.is_line = False
132 else:
133 raise ValidationError({'plot' : 'Plot must be either Line or Bar'})
134 self.plot_type = plot_type
135 self.inverted_series = inverted_series
136 self.normalize_to = normalize_to
137 if self.normalize_to is None:
138 self.normalize_to = ''
139 self.drilldown_callback = drilldown_callback
140
141
142class QualificationHistogram(object):
143 def __init__(self, query, filter_string, interval, drilldown_callback):
144 """
145 query: the main query to retrieve the pass rate information. The first
146 column contains the hostnames of all the machines that satisfied the
147 global filter. The second column (titled 'total') contains the total
148 number of tests that ran on that machine and satisfied the global
149 filter. The third column (titled 'good') contains the number of
150 those tests that passed on that machine.
151
152 filter_string: filter to apply to the common global filter to show the
153 Table View drilldown of a histogram bucket
154
155 interval: interval for each bucket. E.g., 10 means that buckets should
156 be 0-10%, 10%-20%, ...
157
158 """
159 self.query = query
160 self.filter_string = filter_string
161 self.interval = interval
162 self.drilldown_callback = drilldown_callback
163
164
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]165def _create_figure(height_inches):
166 """\
167 Creates an instance of matplotlib.figure.Figure, given the height in inches.
168 Returns the figure and the height in pixels.
169 """
170
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]171 fig = matplotlib.figure.Figure(
172 figsize=(_FIGURE_WIDTH_IN, height_inches + _FIGURE_BOTTOM_PADDING_IN),
173 dpi=_FIGURE_DPI, facecolor='white')
174 fig.subplots_adjust(bottom=float(_FIGURE_BOTTOM_PADDING_IN) / height_inches)
175 return (fig, fig.get_figheight() * _FIGURE_DPI)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]176
177
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]178def _create_line(plots, labels, plot_info):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]179 """\
180 Given all the data for the metrics, create a line plot.
181
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]182 plots: list of dicts containing the plot data. Each dict contains:
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]183 x: list of x-values for the plot
184 y: list of corresponding y-values
185 errors: errors for each data point, or None if no error information
186 available
187 label: plot title
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]188 labels: list of x-tick labels
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]189 plot_info: a MetricsPlot
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]190 """
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]191 # when we're doing any kind of normalization, all series get put into a
192 # single plot
193 single = bool(plot_info.normalize_to)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]194
195 area_data = []
196 lines = []
197 if single:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]198 plot_height = _SINGLE_PLOT_HEIGHT
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]199 else:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]200 plot_height = _MULTIPLE_PLOT_HEIGHT_PER_PLOT * len(plots)
201 figure, height = _create_figure(plot_height)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]202
203 if single:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]204 subplot = figure.add_subplot(1, 1, 1)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]205
206 # Plot all the data
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]207 for plot_index, (plot, color) in enumerate(zip(plots, _colors(len(plots)))):
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]208 needs_invert = (plot['label'] in plot_info.inverted_series)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]209
210 # Add a new subplot, if user wants multiple subplots
211 # Also handle axis inversion for subplots here
212 if not single:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]213 subplot = figure.add_subplot(len(plots), 1, plot_index + 1)
214 subplot.set_title(plot['label'])
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]215 if needs_invert:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]216 # for separate plots, just invert the y-axis
217 subplot.set_ylim(1, 0)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]218 elif needs_invert:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]219 # for a shared plot (normalized data), need to invert the y values
220 # manually, since all plots share a y-axis
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]221 plot['y'] = [-y for y in plot['y']]
222
223 # Plot the series
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]224 subplot.set_xticks(range(0, len(labels)))
225 subplot.set_xlim(-1, len(labels))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]226 if single:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]227 lines += subplot.plot(plot['x'], plot['y'], label=plot['label'],
228 marker=_MULTIPLE_PLOT_MARKER_TYPE,
229 markersize=_MULTIPLE_PLOT_MARKER_SIZE)
230 error_bar_color = lines[-1].get_color()
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]231 else:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]232 lines += subplot.plot(plot['x'], plot['y'], _SINGLE_PLOT_STYLE,
233 label=plot['label'])
234 error_bar_color = _SINGLE_PLOT_ERROR_BAR_COLOR
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]235 if plot['errors']:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]236 subplot.errorbar(plot['x'], plot['y'], linestyle='None',
237 yerr=plot['errors'], color=error_bar_color)
238 subplot.set_xticklabels([])
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]239
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]240 # Construct the information for the drilldowns.
241 # We need to do this in a separate loop so that all the data is in
242 # matplotlib before we start calling transform(); otherwise, it will return
243 # incorrect data because it hasn't finished adjusting axis limits.
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]244 for line in lines:
245
246 # Get the pixel coordinates of each point on the figure
247 x = line.get_xdata()
248 y = line.get_ydata()
249 label = line.get_label()
250 icoords = line.get_transform().transform(zip(x,y))
251
252 # Get the appropriate drilldown query
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]253 drill = plot_info.query_dict['__' + label + '__']
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]254
255 # Set the title attributes (hover-over tool-tips)
256 x_labels = [labels[x_val] for x_val in x]
257 titles = ['%s - %s: %f' % (label, x_label, y_val)
258 for x_label, y_val in zip(x_labels, y)]
259
260 # Get the appropriate parameters for the drilldown query
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]261 params = [dict(query=drill, series=line.get_label(), param=x_label)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]262 for x_label in x_labels]
263
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]264 area_data += [dict(left=ix - 5, top=height - iy - 5,
265 right=ix + 5, bottom=height - iy + 5,
266 title= title,
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]267 callback=plot_info.drilldown_callback,
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]268 callback_arguments=param_dict)
269 for (ix, iy), title, param_dict
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]270 in zip(icoords, titles, params)]
271
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]272 subplot.set_xticklabels(labels, rotation=90, size=_LINE_XTICK_LABELS_SIZE)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]273
274 # Show the legend if there are not multiple subplots
275 if single:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]276 font_properties = matplotlib.font_manager.FontProperties(
277 size=_LEGEND_FONT_SIZE)
278 legend = figure.legend(lines, [plot['label'] for plot in plots],
279 prop=font_properties,
280 handlelen=_LEGEND_HANDLE_LENGTH,
281 numpoints=_LEGEND_NUM_POINTS)
282 # Workaround for matplotlib not keeping all line markers in the legend -
283 # it seems if we don't do this, matplotlib won't keep all the line
284 # markers in the legend.
285 for line in legend.get_lines():
286 line.set_marker(_LEGEND_MARKER_TYPE)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]287
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]288 return (figure, area_data)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]289
290
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]291def _get_adjusted_bar(x, bar_width, series_index, num_plots):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]292 """\
293 Adjust the list 'x' to take the multiple series into account. Each series
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]294 should be shifted such that the middle series lies at the appropriate x-axis
295 tick with the other bars around it. For example, if we had four series
296 (i.e. four bars per x value), we want to shift the left edges of the bars as
297 such:
298 Bar 1: -2 * width
299 Bar 2: -width
300 Bar 3: none
301 Bar 4: width
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]302 """
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]303 adjust = (-0.5 * num_plots - 1 + series_index) * bar_width
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]304 return [x_val + adjust for x_val in x]
305
306
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]307# TODO(showard): merge much of this function with _create_line by extracting and
308# parameterizing methods
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]309def _create_bar(plots, labels, plot_info):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]310 """\
311 Given all the data for the metrics, create a line plot.
312
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]313 plots: list of dicts containing the plot data.
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]314 x: list of x-values for the plot
315 y: list of corresponding y-values
316 errors: errors for each data point, or None if no error information
317 available
318 label: plot title
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]319 labels: list of x-tick labels
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]320 plot_info: a MetricsPlot
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]321 """
322
323 area_data = []
324 bars = []
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]325 figure, height = _create_figure(_SINGLE_PLOT_HEIGHT)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]326
327 # Set up the plot
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]328 subplot = figure.add_subplot(1, 1, 1)
329 subplot.set_xticks(range(0, len(labels)))
330 subplot.set_xlim(-1, len(labels))
331 subplot.set_xticklabels(labels, rotation=90, size=_BAR_XTICK_LABELS_SIZE)
332 # draw a bold line at y=0, making it easier to tell if bars are dipping
333 # below the axis or not.
334 subplot.axhline(linewidth=2, color='black')
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]335
336 # width here is the width for each bar in the plot. Matplotlib default is
337 # 0.8.
338 width = 0.8 / len(plots)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]339
340 # Plot the data
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]341 for plot_index, (plot, color) in enumerate(zip(plots, _colors(len(plots)))):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]342 # Invert the y-axis if needed
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]343 if plot['label'] in plot_info.inverted_series:
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]344 plot['y'] = [-y for y in plot['y']]
345
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]346 adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
347 len(plots))
348 bar_data = subplot.bar(adjusted_x, plot['y'],
349 width=width, yerr=plot['errors'],
350 facecolor=color,
351 label=plot['label'])
352 bars.append(bar_data[0])
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]353
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]354 # Construct the information for the drilldowns.
355 # See comment in _create_line for why we need a separate loop to do this.
356 for plot_index, plot in enumerate(plots):
357 adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,
358 len(plots))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]359
360 # Let matplotlib plot the data, so that we can get the data-to-image
361 # coordinate transforms
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]362 line = subplot.plot(adjusted_x, plot['y'], linestyle='None')[0]
363 label = plot['label']
364 upper_left_coords = line.get_transform().transform(zip(adjusted_x,
365 plot['y']))
366 bottom_right_coords = line.get_transform().transform(
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]367 [(x + width, 0) for x in adjusted_x])
368
369 # Get the drilldown query
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]370 drill = plot_info.query_dict['__' + label + '__']
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]371
372 # Set the title attributes
373 x_labels = [labels[x] for x in plot['x']]
374 titles = ['%s - %s: %f' % (plot['label'], label, y)
375 for label, y in zip(x_labels, plot['y'])]
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]376 params = [dict(query=drill, series=plot['label'], param=x_label)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]377 for x_label in x_labels]
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]378 area_data += [dict(left=ulx, top=height - uly,
379 right=brx, bottom=height - bry,
380 title=title,
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]381 callback=plot_info.drilldown_callback,
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]382 callback_arguments=param_dict)
383 for (ulx, uly), (brx, bry), title, param_dict
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]384 in zip(upper_left_coords, bottom_right_coords, titles,
385 params)]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]386
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]387 figure.legend(bars, [plot['label'] for plot in plots])
388 return (figure, area_data)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]389
390
391def _normalize(data_values, data_errors, base_values, base_errors):
392 """\
393 Normalize the data against a baseline.
394
395 data_values: y-values for the to-be-normalized data
396 data_errors: standard deviations for the to-be-normalized data
397 base_values: list of values normalize against
398 base_errors: list of standard deviations for those base values
399 """
showardd1784472009-06-08 23:29:22 +0000[diff] [blame]400 values = []
401 for value, base in zip(data_values, base_values):
402 try:
403 values.append(100 * (value - base) / base)
404 except ZeroDivisionError:
405 # Base is 0.0 so just simplify:
406 # If value < base: append -100.0;
407 # If value == base: append 0.0 (obvious); and
mbligh1ef218d2009-08-03 16:57:56 +0000[diff] [blame]408 # If value > base: append 100.0.
showardd1784472009-06-08 23:29:22 +0000[diff] [blame]409 values.append(100 * float(cmp(value, base)))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]410
411 # Based on error for f(x,y) = 100 * (x - y) / y
412 if data_errors:
413 if not base_errors:
414 base_errors = [0] * len(data_errors)
showardd1784472009-06-08 23:29:22 +0000[diff] [blame]415 errors = []
416 for data, error, base_value, base_error in zip(
417 data_values, data_errors, base_values, base_errors):
418 try:
419 errors.append(sqrt(error**2 * (100 / base_value)**2
420 + base_error**2 * (100 * data / base_value**2)**2
421 + error * base_error * (100 / base_value**2)**2))
422 except ZeroDivisionError:
423 # Again, base is 0.0 so do the simple thing.
424 errors.append(100 * abs(error))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]425 else:
426 errors = None
427
428 return (values, errors)
429
430
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]431def _create_png(figure):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]432 """\
433 Given the matplotlib figure, generate the PNG data for it.
434 """
435
436 # Draw the image
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]437 canvas = matplotlib.backends.backend_agg.FigureCanvasAgg(figure)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]438 canvas.draw()
439 size = canvas.get_renderer().get_canvas_width_height()
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]440 image_as_string = canvas.tostring_rgb()
441 image = PIL.Image.fromstring('RGB', size, image_as_string, 'raw', 'RGB', 0,
442 1)
443 image_background = PIL.Image.new(image.mode, image.size,
444 figure.get_facecolor())
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]445
446 # Crop the image to remove surrounding whitespace
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]447 non_whitespace = PIL.ImageChops.difference(image, image_background)
448 bounding_box = non_whitespace.getbbox()
449 image = image.crop(bounding_box)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]450
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]451 image_data = StringIO.StringIO()
452 image.save(image_data, format='PNG')
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]453
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]454 return image_data.getvalue(), bounding_box
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]455
456
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]457def _create_image_html(figure, area_data, plot_info):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]458 """\
459 Given the figure and drilldown data, construct the HTML that will render the
460 graph as a PNG image, and attach the image map to that image.
461
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]462 figure: figure containing the drawn plot(s)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]463 area_data: list of parameters for each area of the image map. See the
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]464 definition of the template string '_AREA_TEMPLATE'
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]465 plot_info: a MetricsPlot or QualHistogram
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]466 """
467
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]468 png, bbox = _create_png(figure)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]469
470 # Construct the list of image map areas
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]471 areas = [_AREA_TEMPLATE %
472 (data['left'] - bbox[0], data['top'] - bbox[1],
473 data['right'] - bbox[0], data['bottom'] - bbox[1],
474 data['title'], data['callback'],
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]475 _json_encoder.encode(data['callback_arguments'])
476 .replace('"', '&quot;'))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]477 for data in area_data]
478
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]479 map_name = plot_info.drilldown_callback + '_map'
480 return _HTML_TEMPLATE % (base64.b64encode(png), map_name, map_name,
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]481 '\n'.join(areas))
482
483
484def _find_plot_by_label(plots, label):
485 for index, plot in enumerate(plots):
mbligh1ef218d2009-08-03 16:57:56 +0000[diff] [blame]486 if plot['label'] == label:
487 return index
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]488 raise ValueError('no plot labeled "%s" found' % label)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]489
490
showard84f37322009-03-03 02:16:59 +0000[diff] [blame]491def _normalize_to_series(plots, base_series):
492 base_series_index = _find_plot_by_label(plots, base_series)
493 base_plot = plots[base_series_index]
494 base_xs = base_plot['x']
495 base_values = base_plot['y']
496 base_errors = base_plot['errors']
497 del plots[base_series_index]
498
499 for plot in plots:
500 old_xs, old_values, old_errors = plot['x'], plot['y'], plot['errors']
501 new_xs, new_values, new_errors = [], [], []
502 new_base_values, new_base_errors = [], []
503 # Select only points in the to-be-normalized data that have a
504 # corresponding baseline value
505 for index, x_value in enumerate(old_xs):
506 try:
507 base_index = base_xs.index(x_value)
508 except ValueError:
509 continue
510
511 new_xs.append(x_value)
512 new_values.append(old_values[index])
513 new_base_values.append(base_values[base_index])
514 if old_errors:
515 new_errors.append(old_errors[index])
516 new_base_errors.append(base_errors[base_index])
517
518 if not new_xs:
519 raise NoDataError('No normalizable data for series ' +
520 plot['label'])
521 plot['x'] = new_xs
522 plot['y'] = new_values
523 if old_errors:
524 plot['errors'] = new_errors
525
526 plot['y'], plot['errors'] = _normalize(plot['y'], plot['errors'],
527 new_base_values,
528 new_base_errors)
529
530
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]531def _create_metrics_plot_helper(plot_info, extra_text=None):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]532 """
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]533 Create a metrics plot of the given plot data.
534 plot_info: a MetricsPlot object.
535 extra_text: text to show at the uppper-left of the graph
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]536
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]537 TODO(showard): move some/all of this logic into methods on MetricsPlot
538 """
539 query = plot_info.query_dict['__main__']
Jakob Juelichc5fcff62014-10-14 19:21:09 -0700[diff] [blame^]540 cursor = readonly_connection.cursor()
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]541 cursor.execute(query)
542
543 if not cursor.rowcount:
544 raise NoDataError('query did not return any data')
showard7fc993c2008-09-22 16:21:23 +0000[diff] [blame]545 rows = cursor.fetchall()
546 # "transpose" rows, so columns[0] is all the values from the first column,
547 # etc.
548 columns = zip(*rows)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]549
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]550 plots = []
showard7fc993c2008-09-22 16:21:23 +0000[diff] [blame]551 labels = [str(label) for label in columns[0]]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]552 needs_resort = (cursor.description[0][0] == 'kernel')
553
554 # Collect all the data for the plot
555 col = 1
556 while col < len(cursor.description):
showard7fc993c2008-09-22 16:21:23 +0000[diff] [blame]557 y = columns[col]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]558 label = cursor.description[col][0]
559 col += 1
560 if (col < len(cursor.description) and
561 'errors-' + label == cursor.description[col][0]):
showard7fc993c2008-09-22 16:21:23 +0000[diff] [blame]562 errors = columns[col]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]563 col += 1
564 else:
565 errors = None
566 if needs_resort:
567 y = _resort(labels, y)
568 if errors:
569 errors = _resort(labels, errors)
570
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]571 x = [index for index, value in enumerate(y) if value is not None]
572 if not x:
573 raise NoDataError('No data for series ' + label)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]574 y = [y[i] for i in x]
575 if errors:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]576 errors = [errors[i] for i in x]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]577 plots.append({
578 'label': label,
579 'x': x,
580 'y': y,
581 'errors': errors
582 })
583
584 if needs_resort:
585 labels = _resort(labels, labels)
586
587 # Normalize the data if necessary
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]588 normalize_to = plot_info.normalize_to
589 if normalize_to == 'first' or normalize_to.startswith('x__'):
590 if normalize_to != 'first':
591 baseline = normalize_to[3:]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]592 try:
593 baseline_index = labels.index(baseline)
594 except ValueError:
595 raise ValidationError({
596 'Normalize' : 'Invalid baseline %s' % baseline
597 })
598 for plot in plots:
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]599 if normalize_to == 'first':
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]600 plot_index = 0
601 else:
602 try:
603 plot_index = plot['x'].index(baseline_index)
604 # if the value is not found, then we cannot normalize
605 except ValueError:
606 raise ValidationError({
607 'Normalize' : ('%s does not have a value for %s'
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]608 % (plot['label'], normalize_to[3:]))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]609 })
610 base_values = [plot['y'][plot_index]] * len(plot['y'])
611 if plot['errors']:
612 base_errors = [plot['errors'][plot_index]] * len(plot['errors'])
613 plot['y'], plot['errors'] = _normalize(plot['y'], plot['errors'],
614 base_values,
615 None or base_errors)
616
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]617 elif normalize_to.startswith('series__'):
618 base_series = normalize_to[8:]
showard84f37322009-03-03 02:16:59 +0000[diff] [blame]619 _normalize_to_series(plots, base_series)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]620
621 # Call the appropriate function to draw the line or bar plot
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]622 if plot_info.is_line:
623 figure, area_data = _create_line(plots, labels, plot_info)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]624 else:
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]625 figure, area_data = _create_bar(plots, labels, plot_info)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]626
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]627 # TODO(showard): extract these magic numbers to named constants
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]628 if extra_text:
629 text_y = .95 - .0075 * len(plots)
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]630 figure.text(.1, text_y, extra_text, size='xx-small')
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]631
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]632 return (figure, area_data)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]633
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]634
showardde01b762009-05-01 00:09:11 +0000[diff] [blame]635def create_metrics_plot(query_dict, plot_type, inverted_series, normalize_to,
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]636 drilldown_callback, extra_text=None):
showardde01b762009-05-01 00:09:11 +0000[diff] [blame]637 plot_info = MetricsPlot(query_dict, plot_type, inverted_series,
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]638 normalize_to, drilldown_callback)
639 figure, area_data = _create_metrics_plot_helper(plot_info, extra_text)
640 return _create_image_html(figure, area_data, plot_info)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]641
642
643def _get_hostnames_in_bucket(hist_data, bucket):
644 """\
645 Get all the hostnames that constitute a particular bucket in the histogram.
646
647 hist_data: list containing tuples of (hostname, pass_rate)
648 bucket: tuple containing the (low, high) values of the target bucket
649 """
650
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]651 return [hostname for hostname, pass_rate in hist_data
652 if bucket[0] <= pass_rate < bucket[1]]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]653
654
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]655def _create_qual_histogram_helper(plot_info, extra_text=None):
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]656 """\
657 Create a machine qualification histogram of the given data.
658
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]659 plot_info: a QualificationHistogram
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]660 extra_text: text to show at the upper-left of the graph
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]661
662 TODO(showard): move much or all of this into methods on
663 QualificationHistogram
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]664 """
Jakob Juelichc5fcff62014-10-14 19:21:09 -0700[diff] [blame^]665 cursor = readonly_connection.cursor()
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]666 cursor.execute(plot_info.query)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]667
668 if not cursor.rowcount:
669 raise NoDataError('query did not return any data')
670
671 # Lists to store the plot data.
672 # hist_data store tuples of (hostname, pass_rate) for machines that have
673 # pass rates between 0 and 100%, exclusive.
674 # no_tests is a list of machines that have run none of the selected tests
675 # no_pass is a list of machines with 0% pass rate
676 # perfect is a list of machines with a 100% pass rate
677 hist_data = []
678 no_tests = []
679 no_pass = []
680 perfect = []
681
682 # Construct the lists of data to plot
683 for hostname, total, good in cursor.fetchall():
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]684 if total == 0:
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]685 no_tests.append(hostname)
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]686 continue
687
688 if good == 0:
689 no_pass.append(hostname)
690 elif good == total:
691 perfect.append(hostname)
692 else:
693 percentage = 100.0 * good / total
694 hist_data.append((hostname, percentage))
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]695
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]696 interval = plot_info.interval
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]697 bins = range(0, 100, interval)
698 if bins[-1] != 100:
699 bins.append(bins[-1] + interval)
700
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]701 figure, height = _create_figure(_SINGLE_PLOT_HEIGHT)
702 subplot = figure.add_subplot(1, 1, 1)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]703
704 # Plot the data and get all the bars plotted
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]705 _,_, bars = subplot.hist([data[1] for data in hist_data],
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]706 bins=bins, align='left')
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]707 bars += subplot.bar([-interval], len(no_pass),
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]708 width=interval, align='center')
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]709 bars += subplot.bar([bins[-1]], len(perfect),
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]710 width=interval, align='center')
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]711 bars += subplot.bar([-3 * interval], len(no_tests),
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]712 width=interval, align='center')
713
714 buckets = [(bin, min(bin + interval, 100)) for bin in bins[:-1]]
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]715 # set the x-axis range to cover all the normal bins plus the three "special"
716 # ones - N/A (3 intervals left), 0% (1 interval left) ,and 100% (far right)
717 subplot.set_xlim(-4 * interval, bins[-1] + interval)
718 subplot.set_xticks([-3 * interval, -interval] + bins + [100 + interval])
719 subplot.set_xticklabels(['N/A', '0%'] +
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]720 ['%d%% - <%d%%' % bucket for bucket in buckets] +
721 ['100%'], rotation=90, size='small')
722
723 # Find the coordinates on the image for each bar
724 x = []
725 y = []
726 for bar in bars:
727 x.append(bar.get_x())
728 y.append(bar.get_height())
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]729 f = subplot.plot(x, y, linestyle='None')[0]
730 upper_left_coords = f.get_transform().transform(zip(x, y))
731 bottom_right_coords = f.get_transform().transform(
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]732 [(x_val + interval, 0) for x_val in x])
733
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]734 # Set the title attributes
735 titles = ['%d%% - <%d%%: %d machines' % (bucket[0], bucket[1], y_val)
736 for bucket, y_val in zip(buckets, y)]
737 titles.append('0%%: %d machines' % len(no_pass))
738 titles.append('100%%: %d machines' % len(perfect))
739 titles.append('N/A: %d machines' % len(no_tests))
740
741 # Get the hostnames for each bucket in the histogram
742 names_list = [_get_hostnames_in_bucket(hist_data, bucket)
743 for bucket in buckets]
744 names_list += [no_pass, perfect]
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]745
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]746 if plot_info.filter_string:
747 plot_info.filter_string += ' AND '
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]748
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]749 # Construct the list of drilldown parameters to be passed when the user
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]750 # clicks on the bar.
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]751 params = []
752 for names in names_list:
753 if names:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]754 hostnames = ','.join(_quote(hostname) for hostname in names)
755 hostname_filter = 'hostname IN (%s)' % hostnames
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]756 full_filter = plot_info.filter_string + hostname_filter
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]757 params.append({'type': 'normal',
758 'filterString': full_filter})
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]759 else:
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]760 params.append({'type': 'empty'})
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]761
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]762 params.append({'type': 'not_applicable',
763 'hosts': '<br />'.join(no_tests)})
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]764
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]765 area_data = [dict(left=ulx, top=height - uly,
766 right=brx, bottom=height - bry,
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]767 title=title, callback=plot_info.drilldown_callback,
showard3b2b9302009-04-15 21:53:47 +0000[diff] [blame]768 callback_arguments=param_dict)
769 for (ulx, uly), (brx, bry), title, param_dict
770 in zip(upper_left_coords, bottom_right_coords, titles, params)]
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]771
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]772 # TODO(showard): extract these magic numbers to named constants
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]773 if extra_text:
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]774 figure.text(.1, .95, extra_text, size='xx-small')
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]775
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]776 return (figure, area_data)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]777
778
showardfbdab0b2009-04-29 19:49:50 +0000[diff] [blame]779def create_qual_histogram(query, filter_string, interval, drilldown_callback,
780 extra_text=None):
781 plot_info = QualificationHistogram(query, filter_string, interval,
782 drilldown_callback)
783 figure, area_data = _create_qual_histogram_helper(plot_info, extra_text)
784 return _create_image_html(figure, area_data, plot_info)
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]785
786
787def create_embedded_plot(model, update_time):
788 """\
789 Given an EmbeddedGraphingQuery object, generate the PNG image for it.
790
791 model: EmbeddedGraphingQuery object
792 update_time: 'Last updated' time
793 """
794
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]795 params = pickle.loads(model.params)
showard66b510e2009-05-02 00:45:10 +0000[diff] [blame]796 extra_text = 'Last updated: %s' % update_time
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]797
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]798 if model.graph_type == 'metrics':
showard66b510e2009-05-02 00:45:10 +0000[diff] [blame]799 plot_info = MetricsPlot(query_dict=params['queries'],
800 plot_type=params['plot'],
801 inverted_series=params['invert'],
802 normalize_to=None,
803 drilldown_callback='')
804 figure, areas_unused = _create_metrics_plot_helper(plot_info,
805 extra_text)
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]806 elif model.graph_type == 'qual':
showard66b510e2009-05-02 00:45:10 +0000[diff] [blame]807 plot_info = QualificationHistogram(
808 query=params['query'], filter_string=params['filter_string'],
809 interval=params['interval'], drilldown_callback='')
810 figure, areas_unused = _create_qual_histogram_helper(plot_info,
811 extra_text)
showarde5ae1652009-02-11 23:37:20 +0000[diff] [blame]812 else:
813 raise ValueError('Invalid graph_type %s' % model.graph_type)
814
815 image, bounding_box_unused = _create_png(figure)
816 return image
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]817
818
819_cache_timeout = global_config.global_config.get_config_value(
showard250d84d2010-01-12 21:59:48 +0000[diff] [blame]820 'AUTOTEST_WEB', 'graph_cache_creation_timeout_minutes')
showardce12f552008-09-19 00:48:59 +0000[diff] [blame]821
822
823def handle_plot_request(id, max_age):
824 """\
825 Given the embedding id of a graph, generate a PNG of the embedded graph
826 associated with that id.
827
828 id: id of the embedded graph
829 max_age: maximum age, in minutes, that a cached version should be held
830 """
831 model = models.EmbeddedGraphingQuery.objects.get(id=id)
832
833 # Check if the cached image needs to be updated
834 now = datetime.datetime.now()
835 update_time = model.last_updated + datetime.timedelta(minutes=int(max_age))
836 if now > update_time:
837 cursor = django.db.connection.cursor()
838
839 # We want this query to update the refresh_time only once, even if
840 # multiple threads are running it at the same time. That is, only the
841 # first thread will win the race, and it will be the one to update the
842 # cached image; all other threads will show that they updated 0 rows
843 query = """
844 UPDATE embedded_graphing_queries
845 SET refresh_time = NOW()
846 WHERE id = %s AND (
847 refresh_time IS NULL OR
848 refresh_time + INTERVAL %s MINUTE < NOW()
849 )
850 """
851 cursor.execute(query, (id, _cache_timeout))
852
853 # Only refresh the cached image if we were successful in updating the
854 # refresh time
855 if cursor.rowcount:
856 model.cached_png = create_embedded_plot(model, now.ctime())
857 model.last_updated = now
858 model.refresh_time = None
859 model.save()
860
861 return model.cached_png