def fit_time(measure_type, occtime, season=None):
conn = uo.connect('interval_ion')
with conn:
df_bs = pd.read_sql('SELECT * FROM {0}_id_station'.format(measure_type), conn)
df_area = pd.read_sql('SELECT * FROM area', conn)
df_tz = pd.read_sql('SELECT Building_Number, rawOffset FROM EUAS_timezone', conn)
df_tz.set_index('Building_Number', inplace=True)
df_area.set_index('Building_Number', inplace=True)
bs_pair = zip(df_bs['Building_Number'], df_bs['ICAO'])
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1)
value_lb_dict = {'electric': 'Electric_(KWH)', 'gas':
'Gas_(CubicFeet)'}
multiplier_dict = {'electric': 3.412, 'gas': 1.026}
col = value_lb_dict[measure_type]
m = multiplier_dict[measure_type]
ylabel = {'electric': 'electric (kBtu/sq.ft)', 'gas': 'gas kBtu/sq.ft'}
print len(bs_pair)
sns.set_style("whitegrid")
# palette = sns.cubehelix_palette(len(bs_pair))
palette = sns.color_palette('husl', len(bs_pair))
sns.set_palette(palette)
colors_rgb = [util.float2hex(x) for x in palette]
sns.set_context("talk", font_scale=1)
jsondir = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/json_{0}/'.format(occtime)
# csvdir = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/csv/'
for i, (b, s) in enumerate(bs_pair):
print b, s
try:
area = df_area.ix[b, 'Gross_Sq.Ft']
except KeyError:
print 'No area found'
continue
offset = df_tz.loc[b, 'rawOffset']
df = join_interval(offset, b, s, area, col, m, measure_type, conn, season=season)
df.to_csv(homedir + 'temp/{0}.csv'.format(b))
df = df[df[col] >= 0]
points = df[col]
outliers = show_outlier(points, b, 'upper', measure_type, 1.5)
df['outlier'] = outliers
df = df[~np.array(outliers)]
df['status_week_day_night'] = \
df.apply(lambda r: util.get_status(r['hour'], r['day']), axis=1)
min_time = df['Timestamp'].min()
max_time = df['Timestamp'].max()
gr = df.groupby('status_week_day_night')
bx = plt.axes()
d0 = plot_piece(gr, bx, occtime, colors_rgb[i], measure_type, b, s, scatter=False, annote=True, jsondir=jsondir, season=season)
plt.xlabel('Temperature_F')
# plt.show()
if season is None:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/{0}_{1}.png'.format(measure_type, occtime)
else:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/{0}_{1}_{2}.png'.format(measure_type, occtime, season)
P.savefig(path, dpi = my_dpi, figsize = (2000/my_dpi, 500/my_dpi), bbox_inches='tight')
shutil.copy(path, path.replace('input/FY/interval/ion_0627/piecewise_all', 'plot_FY_weather/html/interval/lean/all'))
plt.close()
return
def fit_time(measure_type, occtime):
conn = uo.connect('interval_ion')
with conn:
df_bs = pd.read_sql('SELECT * FROM {0}_id_station'.format(measure_type), conn)
df_area = pd.read_sql('SELECT * FROM area', conn)
df_area.set_index('Building_Number', inplace=True)
bs_pair = zip(df_bs['Building_Number'], df_bs['ICAO'])
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1)
value_lb_dict = {'electric': 'Electric_(KWH)', 'gas':
'Gas_(CubicFeet)'}
multiplier_dict = {'electric': 3.412, 'gas': 1.026}
col = value_lb_dict[measure_type]
m = multiplier_dict[measure_type]
ylabel = {'electric': 'electric (kBtu/sq.ft)', 'gas': 'gas kBtu/sq.ft'}
print len(bs_pair)
sns.set_style("whitegrid")
# palette = sns.cubehelix_palette(len(bs_pair))
palette = sns.color_palette('husl', len(bs_pair))
sns.set_palette(palette)
colors_rgb = [util.float2hex(x) for x in palette]
sns.set_context("talk", font_scale=1)
jsondir = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/json_{0}/'.format(occtime)
# csvdir = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/csv/'
for i, (b, s) in enumerate(bs_pair)[:5]:
print b, s
try:
area = df_area.ix[b, 'Gross_Sq.Ft']
except KeyError:
print 'No area found'
continue
df = join_interval(b, s, area, col, m, measure_type, conn)
# df.to_csv(homedir + 'temp/{0}.csv'.format(b))
df = df[df[col] >= 0]
points = df[col]
outliers = show_outlier(points, b, 'upper', measure_type, 5)
df['outlier'] = outliers
df = df[~np.array(outliers)]
df['status_week_day_night'] = \
df.apply(lambda r: util.get_status(r['hour'], r['day']), axis=1)
min_time = df['Timestamp'].min()
max_time = df['Timestamp'].max()
gr = df.groupby('status_week_day_night')
bx = plt.axes()
d0 = plot_piece(gr, bx, occtime, colors_rgb[i], measure_type, b, s, scatter=False, annote=True, jsondir=jsondir)
plt.xlabel('Temperature_F')
# plt.show()
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise_all/{0}_{1}.png'.format(measure_type, occtime)
P.savefig(path, dpi = my_dpi, figsize = (2000/my_dpi, 500/my_dpi), bbox_inches='tight')
shutil.copy(path, path.replace('input/FY/interval/ion_0627/piecewise_all', 'plot_FY_weather/html/interval/lean/all'))
plt.close()
return
def fit_time(measure_type, region, season=None):
conn = uo.connect('all')
with conn:
df_bs = pd.read_sql(
'SELECT Building_Number, ICAO, eui_elec, eui_gas, year, month, ave FROM EUAS_monthly_weather',
conn)
df_region = pd.read_sql(
'SELECT DISTINCT Building_Number, [Region_No.] FROM EUAS_monthly',
conn)
if measure_type == 'electric':
good_set = gbs.get_energy_set('eui_elec')
elif measure_type == 'gas':
good_set = gbs.get_energy_set('gas')
df_bs = pd.merge(df_bs, df_region, on='Building_Number', how='left')
df_bs = df_bs[df_bs['Building_Number'].map(lambda x: x in good_set)]
df_bs = df_bs[df_bs['Region_No.'] == str(region)]
df_bs = df_bs[df_bs['eui_elec'].notnull()]
df_bs = df_bs[df_bs['eui_gas'].notnull()]
df_bs.sort_values(by=['Building_Number', 'year', 'month'],
ascending=False,
inplace=True)
bs_pair = list(set(zip(df_bs['Building_Number'], df_bs['ICAO'])))
df_bs['Timestamp'] = df_bs.apply(
lambda r: '{}-{}'.format(int(r['year']), int(r['month'])), axis=1)
sns.set_style("whitegrid")
sns.set_context("talk", font_scale=1)
ylabel = {'electric': 'electric (kBtu/sq.ft)', 'gas': 'gas kBtu/sq.ft'}
col_dict = {'electric': 'eui_elec', 'gas': 'eui_gas'}
print len(bs_pair)
sns.set_style("whitegrid")
# palette = sns.cubehelix_palette(len(bs_pair))
palette = sns.color_palette('husl', len(bs_pair))
sns.set_palette(palette)
colors_rgb = [util.float2hex(x) for x in palette]
sns.set_context("talk", font_scale=1)
jsondir = os.getcwd(
) + '/plot_FY_weather/html/by_region/Region{}/piecewise_all/json/'.format(
region)
title = "Region {}".format(region)
col = col_dict[measure_type]
for i, (b, s) in enumerate(bs_pair):
print b, s
df = df_bs[df_bs['Building_Number'] == b]
df = df.head(n=36)
print df.head()
points = df[col]
min_time = df['Timestamp'].min()
max_time = df['Timestamp'].max()
bx = plt.axes()
d0 = plot_piece(df,
bx,
title,
colors_rgb[i],
measure_type,
b,
s,
scatter=False,
annote=True,
jsondir=jsondir,
season=season)
plt.xlabel('Temperature_F')
# plt.show()
if season is None:
path = os.getcwd(
) + '/plot_FY_weather/html/by_region/Region{}/piecewise_all/{}.png'.format(
region, measure_type)
else:
path = os.getcwd(
) + '/plot_FY_weather/html/by_region/Region{}/piecewise_all/{}_{}.png'.format(
region, measure_type, season)
P.savefig(path,
dpi=my_dpi,
figsize=(2000 / my_dpi, 500 / my_dpi),
bbox_inches='tight')
plt.close()
return
def fit(measure_type, year=None, season=None):
conn = uo.connect('interval_ion')
with conn:
df_bs = pd.read_sql('SELECT * FROM {0}_id_station'.format(measure_type), conn)
df_area = pd.read_sql('SELECT * FROM area', conn)
df_tz = pd.read_sql('SELECT Building_Number, rawOffset FROM EUAS_timezone', conn)
df_tz.set_index('Building_Number', inplace=True)
df_area.set_index('Building_Number', inplace=True)
bs_pair = zip(df_bs['Building_Number'], df_bs['ICAO'])
sns.set_style("whitegrid")
sns.set_palette("Set2", 2)
sns.set_context("talk", font_scale=1)
# col_wrap_dict = {'hour': 6, 'month': 4, 'day': 5, 'status':2}
# upper = {'electric': 600, 'gas': 2500}
value_lb_dict = {'electric': 'Electric_(KWH)', 'gas':
'Gas_(CubicFeet)'}
multiplier_dict = {'electric': 3.412, 'gas': 1.026}
col = value_lb_dict[measure_type]
m = multiplier_dict[measure_type]
ylabel = {'electric': 'electric (kBtu/sq.ft)', 'gas': 'gas kBtu/sq.ft'}
# test = ['TN0088ZZ', 'TX0057ZZ', 'NY0281ZZ', 'NY0304ZZ', 'MO0106ZZ']
# test = ['FL0067ZZ']
# bs_pair = [x for x in bs_pair if x[0] in test]
lines = ['Building_Number,week night save%,weekend day save%,weekend night save%,aggregate save%,CVRMSE week day,CVRMSE week night,CVRMSE weekend day,CVRMSE weekend night']
# bs_pair = bs_pair[:1]
print len(bs_pair)
for b, s in bs_pair:
print b, s
try:
area = df_area.ix[b, 'Gross_Sq.Ft']
except KeyError:
print 'No area found'
continue
offset = df_tz.loc[b, 'rawOffset']
df = join_interval(offset, b, s, area, col, m, measure_type, conn, year, season)
if len(df) == 0:
continue
df.to_csv(homedir + 'temp/{0}.csv'.format(b))
df = df[df[col] >= 0]
points = df[col]
# outliers = show_outlier(points, b, 'upper', measure_type, 5)
outliers = show_outlier(points, b, 'upper', measure_type, 1.5)
df['outlier'] = outliers
df = df[~np.array(outliers)]
df['status_week_day_night'] = \
df.apply(lambda r: util.get_status(r['hour'], r['day']), axis=1)
min_time = df['Timestamp'].min()
max_time = df['Timestamp'].max()
sns.set_style("whitegrid")
colors = sns.color_palette('Paired', 16)
colors_rgb = [util.float2hex(x) for x in colors]
sns.set_context("talk", font_scale=1)
gr = df.groupby('status_week_day_night')
f, axarr = plt.subplots(2, 2, sharex=True, sharey=True)
d0 = plot_piece(gr, axarr[0, 0], 'week day', colors_rgb[0], measure_type, b, s)
if not d0 is None:
axarr[0, 0].set_title('{0} ({3})\nbreak point {1}F, CV(RMSE): {2:.3f}'.format('week day', d0['breakpoint'], d0['CV(RMSE)'], b))
d1 = plot_piece(gr, axarr[0, 1], 'week night', colors_rgb[1], measure_type, b, s)
x0 = d0['x_range'][0]
x1 = d0['x_range'][1]
if type(d0['breakpoint']) == tuple:
b0 = d['breakpoint'][0]
b1 = d['breakpoint'][1]
x = np.array([x0, b0, b1, x1])
else:
x = np.array([x0, d0['breakpoint'], x1])
y = d0['fun'](x, *d0['regression_par'])
d2 = plot_piece(gr, axarr[1, 0], 'weekend day', colors_rgb[2], measure_type, b, s)
d3 = plot_piece(gr, axarr[1, 1], 'weekend night', colors_rgb[3], measure_type, b, s)
axarr[0, 1].plot(x, y, ls='--', c='red')
axarr[1, 0].plot(x, y, ls='--', c='red')
axarr[1, 1].plot(x, y, ls='--', c='red')
plt.ylim((0, 0.02))
plt.xlim((0, 100))
save, err = compute_saving_all(b, d0, d1, d2, d3, axarr)
# save, err = compute_saving_all_setback(d0, d1, d2, d3, axarr)
plt.suptitle('{0} -- {1}'.format(min_time, max_time))
f.text(0.5, 0.04, 'Temperature_F', ha='center', va='center')
if year is None:
if season is None:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise/{1}/{0}_{1}.png'.format(b, measure_type)
else:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise/{1}/{0}_{1}_{2}.png'.format(b, measure_type, season)
else:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise/{1}/{0}_{1}_{2}.png'.format(b, measure_type, int(year))
P.savefig(path, dpi = my_dpi, figsize = (2000/my_dpi, 500/my_dpi), bbox_inches='tight')
shutil.copy(path, path.replace('input/FY/interval/ion_0627/piecewise', 'plot_FY_weather/html/interval/lean'))
plt.close()
lines.append(','.join([b] + save + err))
if year is None:
if season is None:
table_path = os.getcwd() + '/input/FY/interval/ion_0627/table/{0}_save.csv'.format(measure_type)
else:
table_path = os.getcwd() + '/input/FY/interval/ion_0627/table/{0}_save_{1}.csv'.format(measure_type, season)
else:
table_path = os.getcwd() + '/input/FY/interval/ion_0627/table/{0}_save_{1}.csv'.format(measure_type, int(year))
with open(table_path, 'w+') as wt:
wt.write('\n'.join(lines))
return
def fit(measure_type, year=None):
conn = uo.connect('interval_ion')
with conn:
df_bs = pd.read_sql('SELECT * FROM {0}_id_station'.format(measure_type), conn)
df_area = pd.read_sql('SELECT * FROM area', conn)
df_area.set_index('Building_Number', inplace=True)
bs_pair = zip(df_bs['Building_Number'], df_bs['ICAO'])
sns.set_style("whitegrid")
sns.set_palette("Set2", 2)
sns.set_context("talk", font_scale=1)
# col_wrap_dict = {'hour': 6, 'month': 4, 'day': 5, 'status':2}
# upper = {'electric': 600, 'gas': 2500}
value_lb_dict = {'electric': 'Electric_(KWH)', 'gas':
'Gas_(CubicFeet)'}
multiplier_dict = {'electric': 3.412, 'gas': 1.026}
col = value_lb_dict[measure_type]
m = multiplier_dict[measure_type]
ylabel = {'electric': 'electric (kBtu/sq.ft)', 'gas': 'gas kBtu/sq.ft'}
# test = ['TN0088ZZ', 'TX0057ZZ', 'NY0281ZZ', 'NY0304ZZ', 'MO0106ZZ']
# test = ['NM0050ZZ']
# bs_pair = [x for x in bs_pair if x[0] in test]
lines = ['Building_Number,week night save%,weekend day save%,weekend night save%,aggregate save%,CVRMSE week day,CVRMSE week night,CVRMSE weekend day,CVRMSE weekend night']
print len(bs_pair)
# bs_pair = bs_pair[:1]
for b, s in bs_pair:
print b, s
try:
area = df_area.ix[b, 'Gross_Sq.Ft']
except KeyError:
print 'No area found'
continue
df = join_interval(b, s, area, col, m, measure_type, conn, year)
if len(df) == 0:
continue
df.to_csv(homedir + 'temp/{0}.csv'.format(b))
df = df[df[col] >= 0]
points = df[col]
# outliers = show_outlier(points, b, 'upper', measure_type, 5)
outliers = show_outlier(points, b, 'upper', measure_type, 1.5)
df['outlier'] = outliers
df = df[~np.array(outliers)]
df['status_week_day_night'] = \
df.apply(lambda r: util.get_status(r['hour'], r['day']), axis=1)
min_time = df['Timestamp'].min()
max_time = df['Timestamp'].max()
sns.set_style("whitegrid")
colors = sns.color_palette('Paired', 16)
colors_rgb = [util.float2hex(x) for x in colors]
sns.set_context("talk", font_scale=1)
gr = df.groupby('status_week_day_night')
f, axarr = plt.subplots(2, 2, sharex=True, sharey=True)
d0 = plot_piece(gr, axarr[0, 0], 'week day', colors_rgb[0], measure_type, b, s)
if not d0 is None:
axarr[0, 0].set_title('{0}\nbreak point {1}F, CV(RMSE): {2:.3f}'.format('week day', d0['breakpoint'], d0['CV(RMSE)']))
d1 = plot_piece(gr, axarr[0, 1], 'week night', colors_rgb[1], measure_type, b, s)
d2 = plot_piece(gr, axarr[1, 0], 'weekend day', colors_rgb[2], measure_type, b, s)
d3 = plot_piece(gr, axarr[1, 1], 'weekend night', colors_rgb[3], measure_type, b, s)
save, err = compute_saving_all(d0, d1, d2, d3, axarr)
plt.suptitle('{0} -- {1}'.format(min_time, max_time))
f.text(0.5, 0.04, 'Temperature_F', ha='center', va='center')
if year is None:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise/{1}/{0}_{1}.png'.format(b, measure_type)
else:
path = os.getcwd() + '/input/FY/interval/ion_0627/piecewise/{1}/{0}_{1}_{2}.png'.format(b, measure_type, int(year))
P.savefig(path, dpi = my_dpi, figsize = (2000/my_dpi, 500/my_dpi), bbox_inches='tight')
shutil.copy(path, path.replace('input/FY/interval/ion_0627/piecewise', 'plot_FY_weather/html/interval/lean'))
plt.close()
lines.append(','.join([b] + save + err))
if year is None:
table_path = os.getcwd() + '/input/FY/interval/ion_0627/table/{0}_save.csv'.format(measure_type)
else:
table_path = os.getcwd() + '/input/FY/interval/ion_0627/table/{0}_save_{1}.csv'.format(measure_type, int(year))
with open(table_path, 'w+') as wt:
wt.write('\n'.join(lines))
return
