Beispiel #1
0
 def plot(self):
     ''' Plots the used standard load profiles. 
     '''
     plots.latexify(fontsize=12, columns=1)
     #sns.set_context("notebook", font_scale=2, rc={"lines.linewidth": 3})
     print("Plot Winter.")
     ax = self.profiles['Winter'].plot()
     ax.set_ylabel('Factor')
     ax.set_xlabel('Time of Day')
     ax.yaxis.labelpad = 10
     print("Plot Summer.")
     #plt.gca().xaxis.set_major_locator(dates.HourLocator())
     #plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))
     ax = self.profiles['Summer'].plot()
     ax.set_ylabel('Factor')
     ax.set_xlabel('Time of Day')
     ax.yaxis.labelpad = 10
     #plt.gca().xaxis.set_major_locator(dates.HourLocator())
     #plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))
     plt.show()
     i = 1
                                                             merge=True)

def pretty_name_appliance_names(appliance_name_list):
    names = [name.replace('_', ' ')  for name in appliance_name_list]
    names = [name[0].upper() + name[1:] for name in names]
    for old, new in [('Htpc', 'Home theatre PC'), 
                     ('Boiler', 'Gas boiler'),
                     ('Air conditioner', 'Air conditioning')]:
        try:
            names[names.index(old)] = new
        except ValueError:
            pass
    return names

plt.close('all')
latexify(columns=2, fig_height=2)
fig, axes = plt.subplots(ncols=4, subplot_kw={'aspect':1})

count = 0
for dataset_name, dataset_path in DATASETS.iteritems():
    print("\n------------------", dataset_name, "-----------------")
    top_k = proportions[dataset_name][:K]
    print(top_k)
    fracs = top_k.tolist()
    fracs.append(1.0 - sum(fracs))
    labels_raw = top_k.index.tolist()
    labels_raw.append('others')
    colors = get_colors_for_labels(labels_raw)
    pretty_labels = pretty_name_appliance_names(labels_raw)
    explode = (0.03, 0.03, 0.03, 0.03, 0.03, 0.03)
    ax = axes[count]
Beispiel #3
0
            disaggregator_name, t2 - t1))
        disaggregate_time[freq][disaggregator_name] = t2 - t1

        # Predicted power and states
        predicted_power[disaggregator_name] = disaggregator.predictions
        app_ground = test.utility.electric.appliances
        ground_truth_power = pd.DataFrame({appliance: app_ground[appliance][DISAGG_FEATURE] for appliance in app_ground})
        for metric in metrics:
            results[freq][disaggregator_name][metric] = metric_function[
                metric](predicted_power[disaggregator_name], ground_truth_power)


results = results['1T']
start = pd.Timestamp("2013-07-27 21:35")
end = pd.Timestamp("2013-07-27 22:35")
latexify(columns=1)
fig, axes = plt.subplots(ncols=3, sharex=True, sharey=True)


plot_series(ground_truth_power[('air conditioner', 2)]
            [start:end] / 1e3, ax=axes[0], color='k', date_format=DATE_FORMAT, linewidth=0.5)
plot_series(predicted_power['co']
            [('air conditioner', 2)][start:end] / 1e3, ax=axes[1], color='k', date_format=DATE_FORMAT, linewidth=0.5)
plot_series(predicted_power['fhmm']
            [('air conditioner', 2)][start:end] / 1e3, ax=axes[2], color='k', date_format=DATE_FORMAT, linewidth=0.5)


for ax in axes:
    ax.set_ylim((0, 2))
    format_axes(ax)
    ax.set_xticklabels(['0', '   ', '30', '   ', '60'], rotation=0)
            'UKPD': ApplianceName('washing_machine', 1),
            'AMPds': ApplianceName('washer', 1),
            'Pecan': ApplianceName('washer', 1),
            }

time_datasets = {
    'Pecan': [pd.Timestamp("2012-09-05 08:00"), pd.Timestamp("2012-09-05 11:00")],
    'REDD': [pd.Timestamp("2011-05-01 17:43"), pd.Timestamp("2011-05-01 23:00")],
    'AMPds': [pd.Timestamp("2012-04-02 04:30"), pd.Timestamp("2012-04-02 06:00")],
    'iAWE': [pd.Timestamp("2013-07-10 07:22"), pd.Timestamp("2013-07-15 07:30")],
    'UKPD': [pd.Timestamp("2013-10-29 10:47"), pd.Timestamp("2013-10-29 12:17")]

}

count = 0
latexify(columns=1, fig_height=1.5)
fig, axes = plt.subplots(ncols=len(DATASETS), sharey=True)

if LOAD_DATASETS:
    electrics = {}
    for dataset_name, dataset_path in DATASETS.iteritems():
        dataset = DataSet()
        print("Loading", dataset_path)
        dataset.load_hdf5(dataset_path, [1])
        building = dataset.buildings[1]
        electric = building.utility.electric
        electric = electric.sum_split_supplies()
        electrics[dataset_name] = electric

for dataset_name, dataset_path in DATASETS.iteritems():
    electric = electrics[dataset_name]