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plot_action_html.py
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import os
import glob
import pandas as pd
import numpy as np
import seaborn as sns
import pylab as P
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
import textwrap as tw
import datetime
import lean_temperature_monthly as ltm
import util
import label as lb
import util_seq as useq
import lean_dd as ld
import util_io as uo
import get_building_set as gbs
from matplotlib.ticker import MaxNLocator
homedir = os.getcwd() + '/csv_FY/'
master_dir = homedir + 'master_table/'
weatherdir = os.getcwd() + '/csv_FY/weather/'
outputdir = os.getcwd() + '/plot_FY_weather/html/'
def create_index():
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))
return
def process_high_level_links(df_ecm, h):
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])
return building_str
# result_pre[0]: d_gas, result_pre[1]: d_elec
def plot_saving_fromdb(b, s, result_pre, result_post):
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(b, 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(b, 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(b, 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(b, 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()
return d_save, d_cvrmse
def plot_saving(b, s, result_pre, result_post):
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(b, 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(b, 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(b, 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(b, 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()
return d_save, d_cvrmse
def plot_saving_aggyear(b, df, timerange_pre, 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']]
green_face_color = 'lime'
red_face_color = 'red'
# green_face_color = '#94CB89'
# red_face_color = '#D07969'
ylabel_font_name = 'Open Sans'
if theme == 'eui_gas':
c1 = 'brown'
c2 = 'lightsalmon'
# c1 = '#4A4A4A'
# c2 = '#4A4A4A'
# location = 'lower left'
location = 'upper center'
# wrapwidth = 30
wrapwidth = 99
else:
# c1 = '#4A4A4A'
# c2 = '#4A4A4A'
c1 = 'navy'
c2 = 'lightskyblue'
# location = 'upper left'
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'])
time_in_filename = timerange_post.replace(" ", "_")
time_in_filename = time_in_filename.replace("-", "_")
# output data to design students
pd.DataFrame({'month':x, 'actual':y, 'baseline':y_hat}).to_csv("~/Dropbox/gsa_2017/page_data/case_study/{}_{}_{}.csv".format(b, theme, time_in_filename), index=False)
save_percent = round((sum(y_hat) - sum(y)) / sum(y_hat) * 100, 1)
# this is total of monthly mean, the one used in the saving chart
after = sum(y)
before = sum(y_hat)
print after, before
# this is total of all data
# after = sum(np.array(df[theme]))
# before = sum(np.array(df[theme + '_hat']))
# print after, before
print save_percent, after, before, '################'
def time_label(timerange):
if 'before' in timerange or 'after' in timerange:
spaceloc = timerange.find(' ')
year = int(timerange[spaceloc + 1:spaceloc + 5])
return '{}-{}'.format(year, year % 100 + 2)
else:
spaceloc = timerange.rfind(' ')
return "{}--{}".format(timerange[:4], timerange[spaceloc + 3: spaceloc + 5])
# line1, = ax.plot(x, y, c=c1, ls='-', lw=2, marker='o', markersize=5)
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', markersize=5)
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=green_face_color, alpha=0.5, interpolate=True)
ax.fill_between(x, y, y_hat, where=y_hat < y, facecolor=red_face_color,
alpha=0.5, interpolate=True)
ax.set_ylabel("Weather Normalized {} Usage\n(kBtu/sq.ft/month)".format(lb.title_dict[theme]), fontsize=7, fontname=ylabel_font_name)
# ya = ax.get_yaxis()
# ya.set_major_locator(MaxNLocator(integer=True))
# ax.set_ylabel("kBtu per square foot per month", fontsize=10)
legend_label_1 = time_label(timerange_post)
print(legend_label_1)
bboxtop = 1.15
if (b == "DC0007ZZ"):
bboxtop = 1.07
ax.legend([line1, line2],
# ['Actual {1} use in {0}'.format(time_label(timerange_post), lb.title_dict[theme]),
# '\n'.join(tw.wrap('{1} use given {2} habits but {0} weather'.format(time_label(timerange_post),
# lb.title_dict[theme],
# time_label(timerange_pre)),
# ['Actual {1} {0}'.format(time_label(timerange_post), lb.title_dict[theme]),
# '\n'.join(tw.wrap('Predicted {1} past 3 yr performance at {0} weather CVRMSE {2}'.format(time_label(timerange_post), lb.title_dict[theme], round(cvrmse, 2)), wrapwidth)
['Average actual monthly {1} {0}'.format(time_label(timerange_post),lb.title_dict[theme]),
'\n'.join(tw.wrap('Predicted monthly {1} at 3yr prior perf. CVRMSE {2}'.format(time_label(timerange_post), lb.title_dict[theme], round(cvrmse, 2)), wrapwidth)
# put legend below plot
)], loc='upper center', bbox_to_anchor=(0.5, bboxtop), prop={'size': 8}, ncol=2)
# wrapwidth))], loc=9, bbox_to_anchor=(0.5, -0.1))
# wrapwidth))], loc=location)
# following is for region 9 ppt one building
# wrapwidth))], loc=location, fontsize='xx-small')
# plt.rcParams["font.weight"] = "bold"
plt.xticks(range(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], fontsize=8)
# plt.subplots_adjust(right=0.75)
ax.tick_params('y', labelsize=8)
if save_percent > 0:
print('{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)))
# 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)), fontsize=12)
else:
print('{2} usage 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)))
# ax.set_title('{2} usage 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)), fontsize=12, fontweight='bold')
ax.grid(linewidth=0.5)
return save_percent, before, after
def plot_saving_year(df, year, pre_year, theme, ax, cvrmse):
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:
ax.set_title('{2} Savings {0} vs before {4}, {1}% more, CVRMSE: {3}'.format(year, abs(save_percent), lb.title_dict[theme], round(cvrmse, 2), pre_year))
def process_html_lean_saving(b, s, action, pre_start, pre_end,
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:
wt.write(''.join(lines))
def plot_trend_per_dd_fromdb(b, s, breakpoints):
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()
return
def plot_trend_per_dd(b, s, df_energy, breakpoints):
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()
return
def plot_trend_fromdb(b, s, breakpoints):
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()
return
def plot_trend(b, s, df_energy, breakpoints):
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()
return
def plot_action_fromdb():
conn = uo.connect('all')
with conn:
df_action = pd.read_sql('SELECT * FROM EUAS_ecm', conn)
df_pro = pd.read_sql('SELECT * FROM EUAS_ecm_program', conn)
df_bs = pd.read_sql('SELECT * FROM EUAS_monthly_weather', conn)
bs_dict = dict(zip(df_bs['Building_Number'], df_bs['ICAO']))
df_action = df_action[['Building_Number', 'high_level_ECM', 'detail_level_ECM', 'Substantial_Completion_Date']]
df_act = df_action.copy()
df_act = df_act.dropna()
df_act['action'] = df_act.apply(lambda r: r['high_level_ECM'] + ' -- ' + r['detail_level_ECM'], axis=1)
df_act.drop(['high_level_ECM', 'detail_level_ECM'], axis=1,
inplace=True)
gr = df_act.groupby('Building_Number')
names = list(gr.groups)
# print names.index('WA0120BN')
lines = ['Building_Number,Time,Action,Electric_Saving,Gas_Saving,Electric_Before,Electric_After,Gas_Before,Gas_After,Electric_CVRMSE,Gas_CVRMSE']
# FIXME: PA0060ZZ has None in eui_gas
del names[153]
# names = ['CA0154ZZ']
# names = ['CA0306ZZ']
# names = ['IN1703ZZ', 'IL0302ZZ']
# names = ['UT0032ZZ']
# names = ['OK0063ZZ']
df_to_plot = pd.read_csv("~/Dropbox/gsa_2017/input/FY/region_case_study_to_plot.csv")
names = df_to_plot['Building_Number'].tolist()
# names = names + ["NV0294ZZ", "IA0087ZZ"]
# names = ["DC0007ZZ", "NV0294ZZ", "IA0087ZZ"]
# names = ["DC0007ZZ"]
names = ["DE0016ZZ"]
for i, name in enumerate(names):
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_values(by=['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
# df_eng = gr_energy.get_group(b)
# df_eng.reset_index(inplace=True)
s = bs_dict[b]
ar_pair = zip(actions, ranges)
# un-comment if need to plot energy and dd trend side by side
# plot_trend_fromdb(b, s, breakpoints)
# print 'plot trend per dd'
# plot_trend_per_dd_fromdb(b, s, breakpoints)
results = []
for a, r in zip(actions, ranges):
result = ltm.lean_temperature_fromdb(b, s, 2, r, plotPoint=True, 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
def merge_action(string):
string = string.replace('\n', ';')
string = string.replace('pre ', '')
return string
def concat(string):
return string.replace('\n', ';')
for i in range(len(results) - 1):
d_save, d_cvrmse = plot_saving_fromdb(b, s, results[i], results[i + 1])
lines.append(','.join(map(str, [b, breakpoints[i],
# (merge_action(actions[i])),
concat(actionpoints[i]),
d_save['elec_percent'],
d_save['gas_percent'],
d_save['elec_before'],
d_save['elec_after'],
d_save['gas_before'],
d_save['gas_after'],
d_cvrmse['elec'],d_cvrmse['gas']])))
process_html(b, s, results, breakpoints)
with open (os.getcwd() + '/plot_FY_weather/html/table/action_saving.csv', 'w+') as wt:
wt.write('\n'.join(lines))
def plot_action():
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])
# print result
def process_html(b, s, results, breakpoints):
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))
return
def modify_index():
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:
wt.write(''.join(lines))
def table_for_robust_set():
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)
return
def main():
# create_index()
# modify_index()
# plot_action_alone()
# plot_action()
plot_action_fromdb()
# table_for_robust_set()
return