def writePlotData(out,
seriesId,
widthPix=None,
heightPix=None,
minTime=None,
maxTime=None):
if widthPix is None:
widthPix = 800
if heightPix is None:
heightPix = 120
xinch = float(widthPix) / 100
yinch = float(heightPix) / 100
fig = plt.figure()
meta = TIME_SERIES_LOOKUP[seriesId]
queryClass = getClassByName(meta['queryType'])
queryManager = queryClass(meta)
valueClass = getClassByName(meta['valueType'])
valueManager = valueClass(meta, queryManager)
recs = queryManager.getData(minTime=minTime,
maxTime=maxTime)
timestamps = [epochMsToMatPlotLib(queryManager.getTimestamp(rec))
for rec in recs]
vals = [valueManager.getValue(rec)
for rec in recs]
plt.plot(timestamps, vals)
ax = fig.gca()
ax.grid(True)
xmin, xmax, ymin, ymax = ax.axis()
if minTime:
xmin = epochMsToMatPlotLib(minTime)
if maxTime:
xmax = epochMsToMatPlotLib(maxTime)
if ymin == 0 and ymax == 1:
# HACK styling special case
ymin = -0.1
ymax = 1.1
ax.axis([xmin, xmax, ymin, ymax])
pylabUtil.setXAxisDate()
plt.title(queryManager.getValueName(meta['valueField']))
logging.info('writePlotData: writing image')
plt.setp(fig, figwidth=xinch, figheight=yinch)
fig.savefig(out,
format='png',
bbox_inches='tight',
dpi=100,
pad_inches=0.05,
# transparent=True,
)
logging.info('writePlotData: releasing memory')
fig.clf()
plt.close(fig)
def getContourPlotImage(out, x, y, z,
xi, yi, zi,
labelx, labely,
sizePixels,
showSamplePoints=True):
xpix, ypix = sizePixels
xinch, yinch = xpix / 100, ypix / 100
fig = plt.figure()
# suppress outliers
minLevel = percentile(z, 1.0)
maxLevel = percentile(z, 99.0)
logging.info('getContourPlotImage minLevel=%s maxLevel=%s', minLevel, maxLevel)
norm = matplotlib.colors.Normalize(minLevel, maxLevel)
dist = maxLevel - minLevel
minPad = minLevel - 0.05 * dist
maxPad = maxLevel + 0.05 * dist
cappedZi = np.maximum(zi, minPad)
cappedZi = np.minimum(cappedZi, maxPad)
logging.info('getContourPlotImage: plotting data')
ax = fig.gca()
contours = ax.contourf(xi, yi, cappedZi, 256, norm=norm)
pylabUtil.setXAxisDate()
fig.colorbar(contours)
if showSamplePoints:
logging.info('getContourPlotImage: plotting sample points')
# suppress scatterplot points outside the contourf grid
inRange = reduce(np.logical_and,
[xi.min() <= x,
x <= xi.max(),
yi.min() <= y,
y <= yi.max()],
True)
rng = np.where(inRange)
ax.hold(True)
# add scatterplot sample points to figure
ax.scatter(x[rng], y[rng], s=0.5, c='k')
# force the plot limits we want. note the inversion of yi.max() and yi.min()
# so increasing depth is down.
logging.info('getContourPlotImage: configuring axes')
ax.axis([xi.min(), xi.max(), yi.max(), yi.min()])
logging.info('getContourPlotImage: writing image')
plt.setp(fig, figwidth=xinch, figheight=yinch)
fig.savefig(out,
format='png',
bbox_inches='tight',
dpi=100,
pad_inches=0.05,
# transparent=True,
)
logging.info('getContourPlotImage: releasing memory')
fig.clf()
plt.close(fig)