df_ecm = pd.read_csv(master_dir + 'ECM/EUAS_ecm.csv')

df = df_ecm.copy()

df = df[['high_level_ECM', 'detail_level_ECM']]

df2 = df.groupby(['high_level_ECM', 'detail_level_ECM']).count()

df2.drop('high_level_ECM', axis=1, inplace=True)

df2.rename(columns={'detail_level_ECM': 'building count'},

inplace=True)

df2.reset_index(inplace=True)

df2.replace({'detail_level_ECM': {'GSALink': ''}}, inplace=True)

with open(outputdir + 'summary_ecm_count.html', 'w+') as wt:

df2.to_html(wt, index=False, justify='left')

with open(outputdir + 'summary_ecm_count.html', 'r') as rd:

lines = rd.readlines()

high_ecm = df['high_level_ECM'].unique()

high_ecm = [x for x in high_ecm if not (type(x) == float and (np.isnan(x)))]

# high_ecm.remove('GSALink')

print high_ecm

high_count = [len([x for x in lines if "{0}</td>".format(h) in x]) for h in high_ecm]

count_dict = dict(zip(high_ecm, high_count))

print count_dict

for h in high_ecm:

first = True

for i in range(len(lines)):

if ("{0}</td>".format(h) in lines[i]):

if first:

lines[i] = lines[i].replace("<td>", "<td rowspan={0}>".format(count_dict[h]))

lines[i] = lines[i].replace("{0}</td>".format(h), "<a href={1}.html>{0}</a></td>".format(h, h.replace(" ", "_")))

first = False

else:

lines[i] = "\n"

with open(outputdir + 'summary_ecm_count_mergerow.html', 'w+') as wt:

table = ''.join(lines)

wt.write(table)

with open(outputdir + 'index_template.html', 'r') as rd:

rw_lines = rd.readlines()

rw_lines = [x.replace(" <!-- insert table here -->", table) for x in rw_lines]

with open(outputdir + 'index.html', 'w+') as wt:

wt.write(''.join(rw_lines))

with open(outputdir + 'template_action.html', 'r') as rd:

lines = rd.readlines()

df_ecm = df_ecm[['Building Number', 'high_level_ECM', 'Substantial Completion Date']]

df_ecm.drop_duplicates(inplace=True)

print high_ecm

for h in high_ecm:

newlines = [x.replace("action", h) for x in lines]

print h

i = useq.idx_substr(newlines, "<!-- insert list here -->")

assert(i > -1)

bd_str = process_high_level_links(df_ecm, h)

newlines[i] = newlines[i].replace("<!-- insert list here -->",

bd_str)

with open(outputdir + '{0}.html'.format(h.replace(" ", "_")),

'w+') as wt:

wt.write(''.join(newlines))

df = df_ecm.copy()

df = df[df['high_level_ECM'] == h]

buildings = df['Building Number'].tolist()

buildings = sorted(buildings)

building_str = '\n'.join(['<li><a href=single_building/{0}.html>{0}</a></li>'.format(x) for x in buildings])

print 'creating saving plot ...'

pre_args = {}

post_args = {}

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

plotlist = []

d_save = {}

d_cvrmse = {}

if result_pre[0] == None or result_post[0] == None:

print 'no gas data for saving plot'

d_save['gas_percent'] = None

d_save['gas_before'] = None

d_save['gas_after'] = None

d_cvrmse['gas'] = None

else:

plotlist.append('gas')

d_gas_pre = result_pre[0]

pre_args['timerange_pre'] = result_pre[-1]['timerange']

pre_args['base_gas_pre'] = d_gas_pre['base_gas']

pre_args['breakpoint_gas_pre'] = d_gas_pre['breakpoint']

pre_args['df_gas_pre'] = d_gas_pre['df']

d_gas_post = result_post[0]

post_args['base_gas_post'] = d_gas_post['base_gas']

post_args['timerange_post'] = result_post[-1]['timerange']

post_args['breakpoint_gas_post'] = d_gas_post['breakpoint']

d_gas_post['df']['eui_gas_hat'] = d_gas_pre['fun'](np.array(d_gas_post['df']['ave'].tolist()), *d_gas_pre['regression_par'])

post_args['df_gas_post'] = d_gas_post['df']

if result_pre[1] == None or result_post[1] == None:

print 'no elec data for saving plot'

d_save['elec_percent'] = None

d_save['elec_before'] = None

d_save['elec_after'] = None

d_cvrmse['elec'] = None

else:

plotlist.append('elec')

d_elec_pre = result_pre[1]

pre_args['timerange_pre'] = result_pre[-1]['timerange']

pre_args['base_elec_pre'] = d_elec_pre['base_elec']

pre_args['breakpoint_elec_pre'] = d_elec_pre['breakpoint']

d_elec_post = result_post[1]

pre_args['df_elec_pre'] = d_elec_pre['df']

post_args['base_elec_post'] = d_elec_pre['base_elec']

post_args['timerange_post'] = result_post[-1]['timerange']

post_args['breakpoint_elec_post'] = d_elec_post['breakpoint']

d_elec_post['df']['eui_elec_hat'] = d_elec_pre['fun'](np.array(d_elec_post['df']['ave'].tolist()), *d_elec_pre['regression_par'])

post_args['df_elec_post'] = d_elec_post['df']

if len(plotlist) == 2:

fig, (ax_1, ax_2) = plt.subplots(2, 1, sharex=True,

sharey=True)

d_save['elec_percent'], d_save['elec_before'], d_save['elec_after'] = \

plot_saving_aggyear(post_args['df_elec_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_elec', ax_1,

d_elec_pre['CV(RMSE)'])

d_cvrmse['elec'] = round(d_elec_pre['CV(RMSE)'], 3)

ax_1.set_xlim([1, 12])

d_save['gas_percent'], d_save['gas_before'], d_save['gas_after'] = \

plot_saving_aggyear(post_args['df_gas_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_gas', ax_2,

d_gas_pre['CV(RMSE)'])

d_cvrmse['gas'] = round(d_gas_pre['CV(RMSE)'], 3)

ax_2.set_xlim([1, 12])

elif len(plotlist) == 1:

if 'gas' in plotlist:

ax = plt.axes()

d_save['gas_percent'], d_save['gas_before'], d_save['gas_after'] = \

plot_saving_aggyear(post_args['df_gas_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_gas', ax,

d_gas_pre['CV(RMSE)'])

d_cvrmse['gas'] = d_gas_pre['CV(RMSE)']

d_save['elec_percent'] = None

d_save['elec_before'] = None

d_save['elec_after'] = None

d_cvrmse['elec'] = None

ax.set_ylim([0, 7])

ax.set_xlim([1, 12])

elif 'elec' in plotlist:

ax = plt.axes()

d_save['elec_percent'], d_save['elec_before'], d_save['elec_after'] = \

plot_saving_aggyear(post_args['df_elec_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_elec', ax,

d_elec_pre['CV(RMSE)'])

d_cvrmse['elec'] = d_elec_pre['CV(RMSE)']

d_save['gas_percent'] = None

d_save['gas_before'] = None

d_save['gas_after'] = None

d_cvrmse['gas'] = None

ax.set_ylim([0, 7])

ax.set_xlim([1, 12])

else:

return d_save, d_cvrmse

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/savings/{0}_{1}_{2}_agg.png'.format(b, s, post_args['timerange_post']), dpi = 300)

plt.close()

print 'creating saving plot ...'

pre_args = {}

post_args = {}

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

plotlist = []

if result_pre[0] == None or result_post[0] == None:

print 'no gas data for saving plot'

else:

plotlist.append('gas')

d_gas_pre = result_pre[0]

pre_args['timerange_pre'] = result_pre[-1]['timerange']

pre_args['base_gas_pre'] = d_gas_pre['base_gas']

pre_args['breakpoint_gas_pre'] = d_gas_pre['breakpoint']

pre_args['df_gas_pre'] = d_gas_pre['df']

d_gas_post = result_post[0]

post_args['base_gas_post'] = d_gas_post['base_gas']

post_args['timerange_post'] = result_post[-1]['timerange']

post_args['breakpoint_gas_post'] = d_gas_post['breakpoint']

d_gas_post['df']['eui_gas_hat'] = d_gas_pre['fun'](np.array(d_gas_post['df']['ave'].tolist()), *d_gas_pre['regression_par'])

post_args['df_gas_post'] = d_gas_post['df']

if result_pre[1] == None or result_post[1] == None:

print 'no elec data for saving plot'

else:

plotlist.append('elec')

d_elec_pre = result_pre[1]

pre_args['timerange_pre'] = result_pre[-1]['timerange']

pre_args['base_elec_pre'] = d_elec_pre['base_elec']

pre_args['breakpoint_elec_pre'] = d_elec_pre['breakpoint']

d_elec_post = result_post[1]

pre_args['df_elec_pre'] = d_elec_pre['df']

post_args['base_elec_post'] = d_elec_pre['base_elec']

post_args['timerange_post'] = result_post[-1]['timerange']

post_args['breakpoint_elec_post'] = d_elec_post['breakpoint']

d_elec_post['df']['eui_elec_hat'] = d_elec_pre['fun'](np.array(d_elec_post['df']['ave'].tolist()), *d_elec_pre['regression_par'])

post_args['df_elec_post'] = d_elec_post['df']

d_save = {}

d_cvrmse = {}

if len(plotlist) == 2:

fig, (ax_1, ax_2) = plt.subplots(2, 1, sharex=True,

sharey=True)

d_save['elec'] = \

plot_saving_aggyear(post_args['df_elec_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_elec', ax_1,

d_elec_pre['CV(RMSE)'])

d_cvrmse['elec'] = round(d_elec_pre['CV(RMSE)'], 3)

ax_1.set_xlim([1, 12])

d_save['gas'] = plot_saving_aggyear(post_args['df_gas_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_gas', ax_2,

d_gas_pre['CV(RMSE)'])

d_cvrmse['gas'] = round(d_gas_pre['CV(RMSE)'], 3)

ax_2.set_xlim([1, 12])

elif len(plotlist) == 1:

if 'gas' in plotlist:

ax = plt.axes()

d_save['gas'] = \

plot_saving_aggyear(post_args['df_gas_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_gas', ax,

d_gas_pre['CV(RMSE)'])

d_cvrmse['gas'] = d_gas_pre['CV(RMSE)']

d_save['elec'] = None

d_cvrmse['elec'] = None

ax.set_ylim([0, 7])

ax.set_xlim([1, 12])

elif 'elec' in plotlist:

ax = plt.axes()

d_save['elec'] = \

plot_saving_aggyear(post_args['df_elec_post'],

pre_args['timerange_pre'],

post_args['timerange_post'],

'eui_elec', ax,

d_elec_pre['CV(RMSE)'])

d_cvrmse['elec'] = d_elec_pre['CV(RMSE)']

d_save['gas'] = None

d_cvrmse['gas'] = None

ax.set_ylim([0, 7])

ax.set_xlim([1, 12])

else:

return None

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/savings/{0}_{1}_{2}_agg.png'.format(b, s, post_args['timerange_post']), dpi = 300)

plt.close()

timerange_post, theme, ax, cvrmse):

yearcol, timefilter = util.get_time_filter(timerange_post)

df['in_range'] = df[yearcol].map(timefilter)

df = df[df['in_range']]

if theme == 'eui_gas':

c1 = 'brown'

c2 = 'lightsalmon'

location = 'upper center'

wrapwidth = 30

else:

c1 = 'navy'

c2 = 'lightskyblue'

location = 'lower center'

wrapwidth = 99

energy = df.groupby(['month']).mean()

energy.reset_index(inplace=True)

x = np.array(energy['month'])

y = np.array(energy[theme])

y_hat = np.array(energy[theme + '_hat'])

save_percent = round((sum(y_hat) - sum(y)) / sum(y_hat) * 100, 1)

after = sum(y)

before = sum(y_hat)

print save_percent, after, before, '################'

line1, = ax.plot(x, y, c=c1, ls='-', lw=2, marker='o')

line2, = ax.plot(x, y_hat, c=c2, ls='-', lw=2, marker='o')

ax.fill_between(x, y, y_hat, where=y_hat >= y,

facecolor='lime', alpha=0.5, interpolate=True)

ax.fill_between(x, y, y_hat, where=y_hat < y, facecolor='red',

alpha=0.5, interpolate=True)

ax.legend([line1, line2],

['Actual {1} use in {0}'.format(timerange_post, lb.title_dict[theme]), '\n'.join(tw.wrap('{1} use given {2} habits but {0} weather'.format(timerange_post, lb.title_dict[theme], timerange_pre), wrapwidth))], loc=location)

def time_label(timerange):

if 'before' in timerange or 'after' in timerange:

return timerange[timerange.find(' ') + 1:]

else:

return timerange

if save_percent > 0:

ax.set_title('{2} after ({0}) vs before ({4}), {1}% less, CVRMSE: {3}'.format(time_label(timerange_post), abs(save_percent), lb.title_dict[theme], round(cvrmse, 2), time_label(timerange_pre)))

else:

ax.set_title('{2} after ({0}) vs before ({4}), {1}% more, CVRMSE: {3}'.format(time_label(timerange_post), abs(save_percent), lb.title_dict[theme], round(cvrmse, 2), time_label(timerange_pre)))

df = df[df['year'] == year]

if theme == 'eui_gas':

c1 = 'brown'

c2 = 'lightsalmon'

location = 'upper center'

wrapwidth = 30

else:

c1 = 'navy'

c2 = 'lightskyblue'

location = 'lower center'

wrapwidth = 99

x = df['month']

y = df[theme]

y_hat = df[theme + '_hat']

save_percent = round((sum(y_hat) - sum(y)) / sum(y_hat) * 100, 1)

line1, = ax.plot(x, y, c=c1, ls='-', lw=2, marker='o')

line2, = ax.plot(x, y_hat, c=c2, ls='-', lw=2, marker='o')

ax.fill_between(x, y, y_hat, where=y_hat >= y,

facecolor='lime', alpha=0.5,

interpolate=True)

ax.fill_between(x, y, y_hat, where=y_hat < y, facecolor='red',

alpha=0.5, interpolate=True)

ax.legend([line1, line2],

['Actual {1} use in {0}'.format(year, lb.title_dict[theme]), '\n'.join(tw.wrap('{1} use given before {2} habits but {0} weather'.format(year, lb.title_dict[theme], pre_year), wrapwidth))], loc=location)

if save_percent > 0:

ax.set_title('{2} Savings {0} vs before {4}, {1}% less, CVRMSE: {3}'.format(year, save_percent, lb.title_dict[theme], round(cvrmse, 2), pre_year))

else:

post_start, post_end, pre_args,

post_args, years):

with open(os.getcwd() + '/plot_FY_weather/html/savings.html', 'r') as rd:

saving_lines = rd.readlines()

saving_new = []

# saving_lines = ['{0}'.format(x) for x in saving_lines]

for year in years:

new_lines = [x.replace("2012", str(year)) for x in saving_lines]

saving_new += new_lines

print new_lines

saving_new_str = '\n'.join(saving_new)

saving_new_str = saving_new_str.replace('OK0063ZZ_KTUL', '{0}_{1}'.format(b, s))

with open(os.getcwd() + '/plot_FY_weather/html/template_lean_sv.html', 'r') as rd:

lines = rd.readlines()

print (pre_start[-4:])

print int(pre_end[-4:]) - 1

print int(post_start[-4:]) + 1

print (post_end[-4:])

tokens = action.split("--")

# pre_args = [base_gas_pre, base_elec_pre, breakpoint_gas_pre,

# breakpoint_elec_pre]

inside_savings = False

for i, line in enumerate(lines):

lines[i] = lines[i].replace("OK0063ZZ", b)

lines[i] = lines[i].replace("KTUL", s)

lines[i] = lines[i].replace("before CY2011 and after CY2007.png", "before {0} and after {1}.png".format(pre_end, pre_start))

lines[i] = lines[i].replace("before CY2014 and after CY2011.png", "before {0} and after {1}.png".format(post_end, post_start))

lines[i] = lines[i].replace("pre_start -- pre_end", "{0} -- {1}".format(int(pre_start[-4:]), int(pre_end[-4:]) - 1))

lines[i] = lines[i].replace("retrofit year",

str(int(pre_end[-4:])))

lines[i] = lines[i].replace("post_start -- post_end", "{0} -- {1}".format(int(post_start[-4:]) + 1, int(post_end[-4:])))

if not "Building OK0063ZZ did" in lines[i]:

lines[i] = lines[i].replace("action", '<br>'.join(tokens))

else:

lines[i] = lines[i].replace("action", action)

for (args, status) in zip([pre_args, post_args], ['pre retrofit', 'post retrofit']):

lines[i] = lines[i].replace("{0} Base electric load: <br>".format(status), "{0} Base electric load: {1}<br>".format(status, round(args[1], 2)))

lines[i] = lines[i].replace("{0} Base gas load: ".format(status), "{0} Base gas load: {1}".format(status, round(args[0], 2)))

lines[i] = lines[i].replace("{0} Start cooling at: ".format(status), "{0} Start cooling at: {1}".format(status, args[3]))

lines[i] = lines[i].replace("{0} Start heating at: ".format(status), "{0} Start heating at: {1}".format(status, args[2]))

lines[i] = lines[i].replace("<!-- savings -->", saving_new_str)

with open(os.getcwd() +

'/plot_FY_weather/html/single_building/{0}.html'.format(b),

'w+') as wt:

conn = uo.connect('all')

with conn:

df_all = pd.read_sql('SELECT * FROM EUAS_monthly_weather WHERE Building_Number = \'{0}\''.format(b), conn)

conn.close()

df_all.sort(['year', 'month'], inplace=True)

df_all = df_all[['year', 'month', 'eui_elec', 'eui_gas', 'hdd65', 'cdd65']]

df_all = df_all[(df_all['year'] < 2016) & (df_all['year'] > 2002)]

df_agg = df_all.groupby('year').sum()

df_agg.drop('month', axis=1, inplace=True)

df_agg.reset_index(inplace=True)

df_agg['Date'] = df_agg.apply(lambda r: datetime.datetime(int(r['year']), 1, 1) if not np.isnan(r['year']) else np.nan, axis=1)

df_agg['eui_gas_perdd'] = df_agg.apply(lambda r: r['eui_gas'] / r['hdd65'] if r['hdd65'] > 0 else np.nan, axis=1)

df_agg['eui_elec_perdd'] = df_agg.apply(lambda r: r['eui_elec'] / r['cdd65'] if r['cdd65'] > 0 else np.nan, axis=1)

df_agg.set_index('Date', inplace=True)

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

bx = plt.axes()

gas_line_color = '#DE4A50'

elec_line_color = '#429CD5'

ylimit = max(df_agg['eui_gas_perdd'].max(),

df_agg['eui_elec_perdd'].max()) * 1.1

line1, = plt.plot(df_agg.index, df_agg['eui_gas_perdd'], ls='-',

lw=2, marker='o', color=gas_line_color)

line2, = plt.plot(df_agg.index, df_agg['eui_elec_perdd'], ls='-',

lw=2, marker='o', color=elec_line_color)

hdd = df_agg['hdd65'].tolist()

hdd = [int(round(x, 0)) for x in hdd]

cdd = df_agg['cdd65'].tolist()

cdd = [int(round(x, 0)) for x in cdd]

for m, n, d in zip(df_agg.index, df_agg['eui_gas_perdd'], hdd):

bx.annotate('HDD\n{0}'.format(d), xy=(m, n))

for m, n, d in zip(df_agg.index, df_agg['eui_elec_perdd'], cdd):

bx.annotate('CDD\n{0}'.format(d), xy=(m, n))

for bp in breakpoints:

x = pd.to_datetime([pd.to_datetime(bp)] * 2)

plt.plot(x, [0, ylimit], ls='--', lw=2, color='gray')

plt.legend([line1, line2], ['Gas', 'Electric'],

loc='center left', bbox_to_anchor=(1, 0.5),

prop={'size':10})

plt.ylim((0, ylimit))

plt.title("Electric EUI per degree day (65F) and Gas EUI per degree day (65F) Trend")

plt.ylabel("[kBtu/(sq.ft*year*degree day(65F)]")

plt.xlabel("Calendar Year")

# plt.show()

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_year_perdd.png'.format(b, s), dpi = 70)

plt.close()

df_hdd = pd.read_csv(weatherdir + \

'station_dd/{0}_HDD.csv'.format(s))

# df_hdd.sort(['year', 'month'], inplace=True)

df_hdd = df_hdd[['year', 'month', '65F']]

df_cdd = pd.read_csv(weatherdir + \

'station_dd/{0}_CDD.csv'.format(s))

df_cdd = df_cdd[['year', 'month', '65F']]

df_dd = pd.merge(df_hdd, df_cdd, on=['year', 'month'], how='inner', suffixes=['_hdd', '_cdd'])

df_energy = df_energy[df_energy['year'] < 2016]

df_energy = df_energy.groupby(['year']).filter(lambda x: len(x) >

11)

if len(df_energy) < 12:

print 'not enough data for trend plot'

return

df_energy['Date'] = df_energy.apply(lambda r: datetime.datetime(int(r['year']), int(r['month']), 1) if not np.isnan(r['year']) else np.nan, axis=1)

df_all = pd.merge(df_energy, df_dd, on=['year', 'month'],

how='inner')

df_all.sort(['year', 'month'], inplace=True)

df_all = df_all[['year', 'month', 'eui_elec', 'eui_gas', '65F_hdd', '65F_cdd']]

df_agg = df_all.groupby('year').sum()

df_agg.drop('month', axis=1, inplace=True)

df_agg.reset_index(inplace=True)

df_agg['Date'] = df_agg.apply(lambda r: datetime.datetime(int(r['year']), 1, 1) if not np.isnan(r['year']) else np.nan, axis=1)

df_agg['eui_gas_perdd'] = df_agg.apply(lambda r: r['eui_gas'] / r['65F_hdd'] if r['65F_hdd'] > 0 else np.nan, axis=1)

df_agg['eui_elec_perdd'] = df_agg.apply(lambda r: r['eui_elec'] / r['65F_cdd'] if r['65F_cdd'] > 0 else np.nan, axis=1)

df_agg.set_index('Date', inplace=True)

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

bx = plt.axes()

gas_line_color = '#DE4A50'

elec_line_color = '#429CD5'

ylimit = max(df_agg['eui_gas_perdd'].max(),

df_agg['eui_elec_perdd'].max()) * 1.1

line1, = plt.plot(df_agg.index, df_agg['eui_gas_perdd'], ls='-',

lw=2, marker='o', color=gas_line_color)

line2, = plt.plot(df_agg.index, df_agg['eui_elec_perdd'], ls='-',

lw=2, marker='o', color=elec_line_color)

hdd = df_agg['65F_hdd'].tolist()

hdd = [int(round(x, 0)) for x in hdd]

cdd = df_agg['65F_cdd'].tolist()

cdd = [int(round(x, 0)) for x in cdd]

for m, n, d in zip(df_agg.index, df_agg['eui_gas_perdd'], hdd):

bx.annotate('HDD\n{0}'.format(d), xy=(m, n))

for m, n, d in zip(df_agg.index, df_agg['eui_elec_perdd'], cdd):

bx.annotate('CDD\n{0}'.format(d), xy=(m, n))

for bp in breakpoints:

x = pd.to_datetime([pd.to_datetime(bp)] * 2)

y = bx.get_ylim()

plt.plot(x, y, ls='--', lw=2, color='gray')

plt.legend([line1, line2], ['Gas', 'Electric'],

loc='center left', bbox_to_anchor=(1, 0.5),

prop={'size':10})

plt.ylim((0, ylimit))

plt.title("Electric EUI per degree day (65F) and Gas EUI per degree day (65F) Trend")

plt.ylabel("[kBtu/(sq.ft*year*degree day(65F)]")

plt.xlabel("Calendar Year")

# plt.show()

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_year_perdd.png'.format(b, s), dpi = 70)

plt.close()

conn = uo.connect('all')

with conn:

df_all = pd.read_sql('SELECT * FROM EUAS_monthly_weather WHERE Building_Number = \'{0}\''.format(b), conn)

conn.close()

df_all.sort(['year', 'month'], inplace=True)

df_all = df_all[['year', 'month', 'eui_elec', 'eui_gas', 'hdd65', 'cdd65']]

df_all = df_all[(df_all['year'] < 2016) & (df_all['year'] > 2002)]

print type(df_all['eui_gas'].tolist()[0])

byyear = df_all.groupby(['year']).sum()

byyear.index = byyear.index.map(lambda x: datetime.datetime(int(x), 1, 1))

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

bx = plt.axes()

gas_line_color = '#DE4A50'

elec_line_color = '#429CD5'

hdd = byyear['hdd65'].tolist()

hdd = [int(round(x, 0)) for x in hdd]

cdd = byyear['cdd65'].tolist()

cdd = [int(round(x, 0)) for x in cdd]

bx = plt.axes()

line1, = plt.plot(byyear.index, byyear['eui_gas'], ls='-', lw=2,

marker='o', color=gas_line_color)

line2, = plt.plot(byyear.index, byyear['eui_elec'], ls='-', lw=2,

marker='o', color=elec_line_color)

for m, n, d in zip(byyear.index, byyear['eui_gas'], hdd):

bx.annotate('HDD\n{0}'.format(d), xy=(m, n))

for m, n, d in zip(byyear.index, byyear['eui_elec'], cdd):

bx.annotate('CDD\n{0}'.format(d), xy=(m, n))

for bp in breakpoints:

x = pd.to_datetime([pd.to_datetime(bp)] * 2)

y = bx.get_ylim()

plt.plot(x, y, ls='--', lw=2, color='gray')

plt.legend([line1, line2], ['Gas', 'Electric'],

loc='center left', bbox_to_anchor=(1, 0.5),

prop={'size':10})

plt.title("Electric EUI and Gas EUI Trend")

plt.ylabel("[kBtu/sq.ft/year]")

plt.xlabel("Calendar Year")

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_year.png'.format(b, s), dpi = 70)

plt.close()

df_hdd = pd.read_csv(weatherdir + \

'station_dd/{0}_HDD.csv'.format(s))

# df_hdd.sort(['year', 'month'], inplace=True)

df_hdd = df_hdd[['year', 'month', '65F']]

df_cdd = pd.read_csv(weatherdir + \

'station_dd/{0}_CDD.csv'.format(s))

df_cdd = df_cdd[['year', 'month', '65F']]

df_dd = pd.merge(df_hdd, df_cdd, on=['year', 'month'], how='inner', suffixes=['_hdd', '_cdd'])

df_energy = df_energy[df_energy['year'] < 2016]

df_energy = df_energy.groupby(['year']).filter(lambda x: len(x) >

11)

if len(df_energy) < 12:

print 'not enough data for trend plot'

return

df_energy['Date'] = df_energy.apply(lambda r: datetime.datetime(int(r['year']), int(r['month']), 1) if not np.isnan(r['year']) else np.nan, axis=1)

df_all = pd.merge(df_energy, df_dd, on=['year', 'month'],

how='inner')

df_all.sort(['year', 'month'], inplace=True)

byyear = df_all.groupby(['year']).sum()

byyear.index = byyear.index.map(lambda x: datetime.datetime(int(x), 1, 1))

sns.set_style("whitegrid")

sns.set_context("talk", font_scale=1)

bx = plt.axes()

gas_line_color = '#DE4A50'

elec_line_color = '#429CD5'

bx.plot(byyear.index, byyear['65F_hdd'], ls='-', lw=2, marker='o',

color=gas_line_color)

# print type(byyear.index[0])

ylimit = bx.get_ylim()

plt.title('Total HDD (65F) Trend')

plt.xlabel("Calendar Year")

plt.gca().set_ylim(bottom=0)

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_hdd.png'.format(b, s), dpi = 300)

plt.close()

plt.plot(byyear.index, byyear['65F_cdd'], ls='-', lw=2,

marker='o', color=elec_line_color)

plt.title('Total CDD (65F) Trend')

plt.xlabel("Calendar Year")

plt.gca().set_ylim(bottom=0)

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_cdd.png'.format(b, s), dpi = 300)

hdd = byyear['65F_hdd'].tolist()

hdd = [int(round(x, 0)) for x in hdd]

cdd = byyear['65F_cdd'].tolist()

cdd = [int(round(x, 0)) for x in cdd]

df = df_energy.set_index('Date')

line1, = plt.plot(df.index, df['eui_gas'], ls='-', lw=2,

marker='o', color=gas_line_color)

line2, = plt.plot(df.index, df['eui_elec'], ls='-', lw=2,

marker='o', color=elec_line_color)

plt.legend([line1, line2], ['Gas', 'Electric'],

loc='center left', bbox_to_anchor=(1, 0.5),

prop={'size':10})

plt.title("Electric and Gas kBtu/sq.ft. Trend")

plt.ylabel("[kBtu/sq.ft/month]")

plt.xlabel("Calendar Year")

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_month.png'.format(b, s), dpi = 300)

plt.close()

bx = plt.axes()

df2 = df_energy.groupby('year').sum()

df2.reset_index(inplace=True)

df2['Date'] = df2.apply(lambda r: datetime.datetime(int(r['year']), 1, 1) if not np.isnan(r['year']) else np.nan, axis=1)

df2.set_index('Date', inplace=True)

line1, = plt.plot(df2.index, df2['eui_gas'], ls='-', lw=2,

marker='o', color=gas_line_color)

line2, = plt.plot(df2.index, df2['eui_elec'], ls='-', lw=2,

marker='o', color=elec_line_color)

for m, n, d in zip(df2.index, df2['eui_gas'], hdd):

bx.annotate('HDD\n{0}'.format(d), xy=(m, n))

for m, n, d in zip(df2.index, df2['eui_elec'], cdd):

bx.annotate('CDD\n{0}'.format(d), xy=(m, n))

for bp in breakpoints:

x = pd.to_datetime([pd.to_datetime(bp)] * 2)

y = bx.get_ylim()

plt.plot(x, y, ls='--', lw=2, color='gray')

plt.legend([line1, line2], ['Gas', 'Electric'],

loc='center left', bbox_to_anchor=(1, 0.5),

prop={'size':10})

plt.title("Electric EUI and Gas EUI Trend")

plt.ylabel("[kBtu/sq.ft/year]")

plt.xlabel("Calendar Year")

P.savefig(os.getcwd() + '/plot_FY_weather/html/single_building/trend/{0}_{1}_year.png'.format(b, s), dpi = 300)

plt.close()

conn = uo.connect('all')

with conn:

df_region = pd.read_sql('SELECT DISTINCT Building_Number, [Region_No.] FROM EUAS_monthly', conn)

df_bs = pd.read_sql('SELECT * FROM EUAS_monthly_weather', conn)

print len(df_bs)

df_bs = df_bs.merge(df_region, on='Building_Number', how='left')

df_bs = df_bs[df_bs['Region_No.'] == '9']

print len(df_bs)

bs_dict = dict(zip(df_bs['Building_Number'], df_bs['ICAO']))

for b in bs_dict:

s = bs_dict[b]

result = ltm.lean_temperature_fromdb(b, s, 2, timerange=None)

if result == None:

df = pd.read_csv(homedir + 'master_table/ECM/EUAS_ecm.csv')

df = df[['Building Number', 'high_level_ECM', 'detail_level_ECM', 'Substantial Completion Date']]

df_bs = pd.read_csv(homedir + 'master_table/indicator_wECM_weather.csv')

df_bs = df_bs[df_bs['Valid Weather Data'] == 1]

bs_dict = dict(zip(df_bs['Building Number'], df_bs['ICAO']))

print len(df)

df.dropna(inplace=True)

print len(df)

df['action'] = df.apply(lambda r: r['high_level_ECM'] + ' -- ' + r['detail_level_ECM'], axis=1)

df.drop(['high_level_ECM', 'detail_level_ECM'], axis=1,

inplace=True)

gr = df.groupby('Building Number')

names = list(gr.groups)

# print names.index('WA0120BN')

# names = ['CA0168ZZ']

for i, name in enumerate(names[64:]):

print i, name, '222222222222222222222222222222'

group = gr.get_group(name)

df_temp = group.groupby(['Substantial Completion Date'])['action'].apply(lambda x: '\n'.join(x))

df_show = df_temp.to_frame('ECM action')

# df_show['Building Number'] = 'CT0013ZZ'

df_show.reset_index(inplace=True)

df_show['Substantial Completion Date'] = pd.to_datetime(df_show['Substantial Completion Date'])

df_show.sort('Substantial Completion Date', inplace=True)

days_diff = useq.dist_between_adjacent(df_show['Substantial Completion Date'].tolist())

pair = zip(df_show['Substantial Completion Date'].tolist(),

df_show['ECM action'].tolist())

str_pair = [('{0}-{1}-{2}'.format(x[0].year, x[0].month, x[0].day), x[1]) for x in pair]

breakpoints = [x[0] for x in str_pair]

print breakpoints

assert(len(breakpoints) > 0)

ranges = ['before {0}'.format(breakpoints[0])] + useq.merge_adjacent(breakpoints, lambda x, y: '{0} -- {1}'.format(x, y)) + ['after {0}'.format(breakpoints[-1])]

actionpoints = [x[1] for x in str_pair]

actions = ['pre {0}'.format(actionpoints[0])] + useq.merge_adjacent(actionpoints, lambda x, y: 'post {0} pre {1}'.format(x, y)) + ['post {0}'.format(actionpoints[-1])]

b = name

if b in bs_dict:

s = bs_dict[b]

else:

print 'no weather data'

continue

ar_pair = zip(actions, ranges)

# FIXME populate energy and weather data

df_eng = pd.read_csv(weatherdir + \

'energy_temp/{0}_{1}.csv'.format(b, s))

# un-comment if need to plot energy and dd trend side by side

plot_trend(b, s, df_eng, breakpoints)

print 'plot trend per dd'

plot_trend_per_dd(b, s, df_eng, breakpoints)

results = []

for a, r in zip(actions, ranges):

result = ltm.lean_temperature(b, s, 2, r, action=a)

if result == None:

result = (None, None, None)

d = {'building': b, 'station': s, 'timerange': r, 'action': a}

result += tuple([d])

results.append(result)

length = len(results)

if length == 0:

print 'no lean plot generated'

continue

for i in range(len(results) - 1):

plot_saving(b, s, results[i], results[i + 1])

process_html(b, s, results)

# # Degree day version

# results = []

# for a, r in zip(actions, ranges):

# result = ld.plot_lean_saving_one(b, s, r)

# results.append(result)

# length = len(results)

# if length == 0:

# print 'no lean plot generated'

# continue

# for i in range(len(results) - 1):

# ld.plot_saving(b, s, results[i], results[i + 1])

with open(os.getcwd() + \

'/plot_FY_weather/html/single_building/template_singlebuilding.html', 'r') as rd:

lines = rd.readlines()

def format_action(action, time):

action_list = action.split('\n')

action_pair_list = [x.split(' -- ') for x in action_list]

action_sort = sorted(action_pair_list, key=lambda x: (x[0],

x[1]))

action_init = action_sort[0][0]

string = '{0}\n\n'.format(time)

string += action_init

for i in range(len(action_sort)):

action_now = action_sort[i][0]

if action_now == action_init:

string += ('\n ' + action_sort[i][1])

else:

string += ('\n' + action_sort[i][0])

string += ('\n ' + action_sort[i][1])

action_init = action_now

string = string.replace('GSALink\n GSALink', 'GSALink')

return string

def substitute_data(results, j, i, end):

if results[j][0] == None:

base_gas = '****'

bpoint_gas = '****'

else:

base_gas = str(round(results[j][0]['base_gas'], 2))

bpoint_gas = str(int(results[j][0]['breakpoint']))

if results[j][1] == None:

base_elec = '****'

bpoint_elec = '****'

else:

base_elec = str(round(results[j][1]['base_elec'], 2))

bpoint_elec = str(int(results[j][1]['breakpoint']))

data_lines = lines[i + 1: i + end]

data = ''.join(data_lines)

data = data.replace('base_elec', base_elec)

data = data.replace('base_gas', base_gas)

data = data.replace('bpoint_elec', bpoint_elec, 2)

data = data.replace('bpoint_gas', bpoint_gas, 2)

return data

for i, line in enumerate(lines):

lines[i] = lines[i].replace("IA0112ZZ", b)

lines[i] = lines[i].replace("KMXO", s)

if "substitute period" in lines[i]:

newlines = []

for j in range(len(results) - 1):

action = results[j][-1]['action']

action = action[action.find('pre') + 4:]

action = format_action(action, breakpoints[j])

newlines.append(lines[i + 1].replace("action", action))

newlines.append(lines[i + 2].replace("building_station_period", "{0}_{1}_{2}".format(b, s, results[j + 1][-1]['timerange'])))

str_newlines = ''.join(newlines)

lines[i + 1] = ''

lines[i + 2] = str_newlines

if "substitute lean period" in lines[i]:

newlines = []

for j in range(len(results) - 1):

newlines.append(lines[i + 1].replace("building_station_combined_period", "{0}_{1}_combined_{2}".format(b, s, results[j][-1]['timerange'])))

action = results[j][-1]['action']

action = action[action.find('pre') + 4:]

action = format_action(action, breakpoints[j])

newlines.append(lines[i + 2].replace("action", action))

newlines.append(lines[i + 1].replace("building_station_combined_period", "{0}_{1}_combined_{2}".format(b, s, results[-1][-1]['timerange'])))

str_newlines = ''.join(newlines)

lines[i + 1] = str_newlines

lines[i + 2] = ''

if "substitute lean data" in lines[i]:

newlines = []

for j in range(len(results) - 1):

# if results[j][0] == None or results[j][1] == None:

# return 'No data'

data = substitute_data(results, j, i, 8)

newlines.append(data)

data_last = substitute_data(results, -1, i, 7)

newlines.append(data_last)

str_newlines = ''.join(newlines)

lines[i + 1] = str_newlines

lines[i + 2: i + 9] = ''

with open(os.getcwd() + \

'/plot_FY_weather/html/single_building/{0}.html'.format(b), 'w+') as wt:

print 'write to html: {0}.html ...'.format(b)

wt.write(''.join(lines))

actions = ['Advanced_Metering', 'Building_Envelope',

'Building_Tuneup_or_Utility_Improvements', 'HVAC',

'Lighting', 'GSALink']

pages = [os.getcwd() + '/plot_FY_weather/html/{0}.html'.format(x)

for x in actions]

htmls = glob.glob(os.getcwd() + \

'/plot_FY_weather/html/single_building/*.html')

buildings = [x[x.rfind('/') + 1: -5] for x in htmls]

for p in pages:

with open (p, 'r') as rd:

lines = rd.readlines()

for i, line in enumerate(lines):

idx = line.find('/') + 1

b = line[idx: idx + 8]

if ('.html' in line) and not (b in buildings):

lines[i] = lines[i].replace("</a>", " ... data coming"+

" soon ...</a>")

with open (p, 'w+') as wt:

conn = uo.connect('all')

study_set = gbs.get_energy_set('eui').intersection(gbs.get_cat_set(['A', 'I'], conn))

df = pd.read_csv(os.getcwd() + '/plot_FY_weather/html/table/action_saving.csv')

df = df[df['Building_Number'].isin(study_set)]

df.sort('Building_Number', inplace=True)

df.to_csv(os.getcwd() + '/plot_FY_weather/html/table/action_saving_robustset.csv', index=False)

# create_index()

# modify_index()

# plot_action_alone()

# plot_action()

plot_lean_by_reion(9)

# table_for_robust_set()