def plot_ts(series, lib="matplotlib"): #*args if lib == "matplotlib": import matplotlib.pyplot as plt import scikits.timeseries.lib.plotlib as tpl fig = tpl.tsfigure() fsp = fig.add_tsplot(111) fsp.tsplot(series, '-') plt.show() else: # guiqwt #raise NotImplementedError pass
import numpy as np import matplotlib.pyplot as plt import scikits.timeseries as ts import scikits.timeseries.lib.plotlib as tpl # generate some random data num_points = 12 data = np.cumprod(1 + np.random.normal(0, 1, num_points) / 100) series = ts.time_series(data, start_date=ts.now('d') - num_points) fig = tpl.tsfigure() fsp = fig.add_tsplot(111) fsp.tsplot(series, '-') fsp.set_xlim(int(series.start_date), int(series.end_date)) fsp.set_title('%i days' % num_points) plt.show()
import scikits.timeseries as ts import scikits.timeseries.lib.plotlib as tplt data = sm.datasets.macrodata.load(as_pandas=False) data = data.data ### Create Timeseries Representations of a few vars dates = ts.date_array(start_date=ts.Date('Q', year=1959, quarter=1), end_date=ts.Date('Q', year=2009, quarter=3)) ts_data = data[['realgdp', 'realcons', 'cpi']].view(float).reshape(-1, 3) ts_data = np.column_stack((ts_data, (1 - data['unemp'] / 100) * data['pop'])) ts_series = ts.time_series(ts_data, dates) fig = tplt.tsfigure() fsp = fig.add_tsplot(221) fsp.tsplot(ts_series[:, 0], '-') fsp.set_title("Real GDP") fsp = fig.add_tsplot(222) fsp.tsplot(ts_series[:, 1], 'r-') fsp.set_title("Real Consumption") fsp = fig.add_tsplot(223) fsp.tsplot(ts_series[:, 2], 'g-') fsp.set_title("CPI") fsp = fig.add_tsplot(224) fsp.tsplot(ts_series[:, 3], 'y-') fsp.set_title("Employment") # Plot real GDP #plt.subplot(221)
import numpy as np import matplotlib.pyplot as plt import scikits.timeseries as ts import scikits.timeseries.lib.plotlib as tpl # generate some random data num_points = 250 data = np.cumprod(1 + np.random.normal(0, 1, num_points)/100) series = ts.time_series(data, start_date=ts.now('d')-num_points) fig = tpl.tsfigure() fsp = fig.add_tsplot(111) fsp.tsplot(series, '-') fsp.set_xlim(int(series.start_date), int(series.end_date)) fsp.set_title('%i days' % num_points) plt.show()
data = sm.datasets.macrodata.load(as_pandas=False) data = data.data ### Create Timeseries Representations of a few vars dates = ts.date_array(start_date=ts.Date('Q', year=1959, quarter=1), end_date=ts.Date('Q', year=2009, quarter=3)) ts_data = data[['realgdp','realcons','cpi']].view(float).reshape(-1,3) ts_data = np.column_stack((ts_data, (1 - data['unemp']/100) * data['pop'])) ts_series = ts.time_series(ts_data, dates) fig = tplt.tsfigure() fsp = fig.add_tsplot(221) fsp.tsplot(ts_series[:,0],'-') fsp.set_title("Real GDP") fsp = fig.add_tsplot(222) fsp.tsplot(ts_series[:,1],'r-') fsp.set_title("Real Consumption") fsp = fig.add_tsplot(223) fsp.tsplot(ts_series[:,2],'g-') fsp.set_title("CPI") fsp = fig.add_tsplot(224) fsp.tsplot(ts_series[:,3],'y-') fsp.set_title("Employment")
continue dates.append(row.split(',')[0]) #dates = data[:, 0].astype(int).tolist() dates = [parse_date(str(d)) for d in dates] ELECTRIC = time_series(masked_less(data[:, 0], 0), dates) GAS = time_series(masked_less(data[:, 1], 0), dates) WATER = time_series(masked_less(data[:, 2], 0), dates) START = dates[0] END = dates[-1] CLIMATE = get_nm_climate( start_date='%i%.2i%.2i' % (START.year, START.month, START.day), end_date='%i%.2i%.2i' % (END.year, END.month, END.day)) CLIMATE.tofile('/tmp/test.txt') MAX_TEMP, MIN_TEMP, ACCUM_PRECIP = CLIMATE.split() FIG = tpl.tsfigure(figsize=(10, 7)) FIG.subplots_adjust(hspace=0.1) GAS_PLOT = FIG.add_tsplot(313) GAS_PLOT.tsplot(backward_fill(GAS), zorder=20) GAS_PLOT.set_ylabel('Gas usage, therms') GAS_PLOT.grid(linestyle='-', color='0.9', zorder=0) TEMP_PLOT = GAS_PLOT.add_yaxis(position='right') TEMP_PLOT.tsplot(MIN_TEMP, color='0.5', zorder=10) TEMP_PLOT.tsplot(MAX_TEMP, color='0.5', zorder=10) TEMP_PLOT.set_ylabel(u'Temperature range, \u2109F') WATER_PLOT = FIG.add_tsplot(311, sharex=GAS_PLOT) WATER_PLOT.tsplot(backward_fill(WATER * 748), zorder=20) WATER_PLOT.grid(linestyle='-', color='0.9', zorder=1) WATER_PLOT.set_xticklabels(WATER_PLOT.get_xticklabels(), visible=False)
print i except: pass #debugging plot below, make sure parameters match the ones actually used gate = DmGateway() #gate = StaticGateway("staticDataSet.json") ps = Parser() dset = ps.parse(time_series) series = ts.time_series(dset[1].getMaskedArray(), start_date=ts.Date(freq='year', year=1, month=1)) #print series.count fig = tplot.tsfigure() # 111 = width, height, subplots fsp = fig.add_tsplot(111) fsp.tsplot(series, '-') np.seterr(invalid='raise') try: avg = mov_average(series, 20) std = mov_std(series, 20) except Exception as error: print error traceback.print_exc() print series print avg print std lowerlim = avg+std*2 upperlim = avg-std*2