analysis/webservice/algorithms/TimeSeriesSolr.py [181:280]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def toImage(self): type = self.computeOptions().get_plot_type() if type == TimeSeriesResults.LINE_PLOT or type == "default": return self.createLinePlot() elif type == TimeSeriesResults.SCATTER_PLOT: return self.createScatterPlot() else: raise Exception("Invalid or unsupported time series plot specified") def createScatterPlot(self): timeSeries = [] series0 = [] series1 = [] res = self.results() meta = self.meta() plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None if plotSeries is None: plotSeries = "mean" for m in res: if len(m) == 2: timeSeries.append(datetime.fromtimestamp(m[0]["time"] / 1000)) series0.append(m[0][plotSeries]) series1.append(m[1][plotSeries]) title = ', '.join(set([m['title'] for m in meta])) sources = ', '.join(set([m['source'] for m in meta])) dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y')) fig, ax = plt.subplots() fig.set_size_inches(11.0, 8.5) ax.scatter(series0, series1, alpha=0.5) ax.set_xlabel(meta[0]['units']) ax.set_ylabel(meta[1]['units']) ax.set_title("%s\n%s\n%s" % (title, sources, dateRange)) par = np.polyfit(series0, series1, 1, full=True) slope = par[0][0] intercept = par[0][1] xl = [min(series0), max(series0)] yl = [slope * xx + intercept for xx in xl] plt.plot(xl, yl, '-r') # r = self.stats()["r"] # plt.text(0.5, 0.5, "r = foo") ax.grid(True) fig.tight_layout() sio = StringIO() plt.savefig(sio, format='png') return sio.getvalue() def createLinePlot(self): nseries = len(self.meta()) res = self.results() meta = self.meta() timeSeries = [datetime.fromtimestamp(m[0]["time"] / 1000) for m in res] means = [[np.nan] * len(res) for n in range(0, nseries)] plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None if plotSeries is None: plotSeries = "mean" for n in range(0, len(res)): timeSlot = res[n] for seriesValues in timeSlot: means[seriesValues['ds']][n] = seriesValues[plotSeries] x = timeSeries fig, axMain = plt.subplots() fig.set_size_inches(11.0, 8.5) fig.autofmt_xdate() title = ', '.join(set([m['title'] for m in meta])) sources = ', '.join(set([m['source'] for m in meta])) dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y')) axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange)) axMain.set_xlabel('Date') axMain.grid(True) axMain.xaxis.set_major_locator(mdates.YearLocator()) axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y')) axMain.xaxis.set_minor_locator(mdates.MonthLocator()) axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d') plots = [] for n in range(0, nseries): if n == 0: ax = axMain else: ax = ax.twinx() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - analysis/webservice/algorithms_spark/TimeSeriesSpark.py [341:440]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def toImage(self): type = self.computeOptions().get_plot_type() if type == TimeSeriesResults.LINE_PLOT or type == "default": return self.createLinePlot() elif type == TimeSeriesResults.SCATTER_PLOT: return self.createScatterPlot() else: raise Exception("Invalid or unsupported time series plot specified") def createScatterPlot(self): timeSeries = [] series0 = [] series1 = [] res = self.results() meta = self.meta() plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None if plotSeries is None: plotSeries = "mean" for m in res: if len(m) == 2: timeSeries.append(datetime.fromtimestamp(m[0]["time"] / 1000)) series0.append(m[0][plotSeries]) series1.append(m[1][plotSeries]) title = ', '.join(set([m['title'] for m in meta])) sources = ', '.join(set([m['source'] for m in meta])) dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y')) fig, ax = plt.subplots() fig.set_size_inches(11.0, 8.5) ax.scatter(series0, series1, alpha=0.5) ax.set_xlabel(meta[0]['units']) ax.set_ylabel(meta[1]['units']) ax.set_title("%s\n%s\n%s" % (title, sources, dateRange)) par = np.polyfit(series0, series1, 1, full=True) slope = par[0][0] intercept = par[0][1] xl = [min(series0), max(series0)] yl = [slope * xx + intercept for xx in xl] plt.plot(xl, yl, '-r') # r = self.stats()["r"] # plt.text(0.5, 0.5, "r = foo") ax.grid(True) fig.tight_layout() sio = StringIO() plt.savefig(sio, format='png') return sio.getvalue() def createLinePlot(self): nseries = len(self.meta()) res = self.results() meta = self.meta() timeSeries = [datetime.fromtimestamp(m[0]["time"] / 1000) for m in res] means = [[np.nan] * len(res) for n in range(0, nseries)] plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None if plotSeries is None: plotSeries = "mean" for n in range(0, len(res)): timeSlot = res[n] for seriesValues in timeSlot: means[seriesValues['ds']][n] = seriesValues[plotSeries] x = timeSeries fig, axMain = plt.subplots() fig.set_size_inches(11.0, 8.5) fig.autofmt_xdate() title = ', '.join(set([m['title'] for m in meta])) sources = ', '.join(set([m['source'] for m in meta])) dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y')) axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange)) axMain.set_xlabel('Date') axMain.grid(True) axMain.xaxis.set_major_locator(mdates.YearLocator()) axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y')) axMain.xaxis.set_minor_locator(mdates.MonthLocator()) axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d') plots = [] for n in range(0, nseries): if n == 0: ax = axMain else: ax = ax.twinx() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -