def import_from_cache(self, cached_stat, sections):
# we (deliberately) use duplicate indices to cache GoodSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for tf_start, df_grouped_by_index in grouped_by_index:
grouped_by_end = df_grouped_by_index.groupby('end')
for tf_end, sections_df in grouped_by_end:
end = tz_localize_naive(tf_end, tz)
timeframe = TimeFrame(tf_start, end)
if timeframe in sections:
timeframes = []
for _, row in sections_df.iterrows():
#~ print('Computing sections...')
#~ print('\n=== type(row)\n%s\n' % (type(row)))
#~ print('\n=== row\n%s\n' % (row))
#~ print('\n=== dir(row)\n%s\n' % (dir(row)))
#~ print('\n=== row.__dict__\n%s\n' % (row.__dict__))
#~ print('\n=== row.index\n%s\n' % (row.index))
#~ print('\n=== row.index.__dict__\n%s\n' % (row.index.__dict__))
#~ print('\n=== dir(row.index)\n%s\n' % (dir(row.index)))
#~ print('\n=== row.index[2]\n%s\n' % (row.index[2]))
#~ print('\n=== row.iloc[1]\n%s\n' % (row.iloc[1]))
#~ print('\n=== row.iloc[2]\n%s\n' % (row.iloc[2]))
section_start = tz_localize_naive(
row.iloc[2], tz) # row['section_start']
section_end = tz_localize_naive(
row.iloc[1], tz) # row['section_end']
timeframes.append(TimeFrame(section_start,
section_end))
self.append(timeframe, {'sections': [timeframes]})
def import_from_cache(self, cached_stat, sections):
# we (deliberately) use duplicate indices to cache GoodSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for name, group in grouped_by_index:
assert group['end'].unique().size == 1
end = tz_localize_naive(group['end'].iloc[0], tz)
timeframe = TimeFrame(name, end)
if timeframe in sections:
timeframes = []
for _, row in group.iterrows():
section_start = tz_localize_naive(row['section_start'], tz)
section_end = tz_localize_naive(row['section_end'], tz)
timeframes.append(TimeFrame(section_start, section_end))
self.append(timeframe, {'sections': [timeframes]})
def import_from_cache(self, cached_stat, sections):
# we (deliberately) use duplicate indices to cache GoodSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for tf_start, df_grouped_by_index in grouped_by_index:
grouped_by_end = df_grouped_by_index.groupby('end')
for tf_end, sections_df in grouped_by_end:
end = tz_localize_naive(tf_end, tz)
timeframe = TimeFrame(tf_start, end)
if timeframe in sections:
timeframes = []
for _, row in sections_df.iterrows():
section_start = tz_localize_naive(row['section_start'], tz)
section_end = tz_localize_naive(row['section_end'], tz)
timeframes.append(TimeFrame(section_start, section_end))
self.append(timeframe, {'sections': [timeframes]})
def import_from_cache(self, cached_stat, sections):
# we (deliberately) use duplicate indices to cache GoodSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for name, group in grouped_by_index:
assert group['end'].unique().size == 1
end = tz_localize_naive(group['end'].iloc[0], tz)
timeframe = TimeFrame(name, end)
if timeframe in sections:
timeframes = []
for _, row in group.iterrows():
section_start = tz_localize_naive(row['section_start'], tz)
section_end = tz_localize_naive(row['section_end'], tz)
timeframes.append(TimeFrame(section_start, section_end))
self.append(timeframe, {'sections': [timeframes]})
def import_from_cache(self, cached_stat, sections):
# we (deliberately) use duplicate indices to cache GoodSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for tf_start, df_grouped_by_index in grouped_by_index:
grouped_by_end = df_grouped_by_index.groupby('end')
for tf_end, sections_df in grouped_by_end:
end = tz_localize_naive(tf_end, tz)
timeframe = TimeFrame(tf_start, end)
if timeframe in sections:
timeframes = []
for _, row in sections_df.iterrows():
section_start = tz_localize_naive(
row['section_start'], tz)
section_end = tz_localize_naive(row['section_end'], tz)
timeframes.append(TimeFrame(section_start,
section_end))
self.append(timeframe, {'sections': [timeframes]})
def import_from_cache(self, cached_stat, sections):
"""
Parameters
----------
cached_stat : DataFrame of cached data
sections : list of nilmtk.TimeFrame objects
describing the sections we want to load stats for.
"""
tz = get_tz(cached_stat)
usable_sections_from_cache = []
def append_row(row, section):
# Save, disable, then re-enable `pd.SettingWithCopyWarning`
# from http://stackoverflow.com/a/20627316/732596
chained_assignment = pd.options.mode.chained_assignment
pd.options.mode.chained_assignment = None
# We stripped off the timezone when exporting to cache
# so now we must put the timezone back.
row['end'] = tz_localize_naive(row['end'], tz)
pd.options.mode.chained_assignment = chained_assignment
if row['end'] == section.end:
usable_sections_from_cache.append(row)
for section in sections:
if not section:
continue
try:
rows_matching_start = cached_stat.loc[section.start]
except KeyError:
pass
else:
if isinstance(rows_matching_start, pd.Series):
append_row(rows_matching_start, section)
else:
for row_i in range(rows_matching_start.shape[0]):
row = rows_matching_start.iloc[row_i]
append_row(row, section)
self._data = pd.DataFrame(usable_sections_from_cache)
self._data.sort_index(inplace=True)
def import_from_cache(self, cached_stat, sections):
"""
Parameters
----------
cached_stat : DataFrame of cached data
sections : list of nilmtk.TimeFrame objects
describing the sections we want to load stats for.
"""
tz = get_tz(cached_stat)
usable_sections_from_cache = []
def append_row(row, section):
# Save, disable, then re-enable `pd.SettingWithCopyWarning`
# from http://stackoverflow.com/a/20627316/732596
chained_assignment = pd.options.mode.chained_assignment
pd.options.mode.chained_assignment = None
# We stripped off the timezone when exporting to cache
# so now we must put the timezone back.
row['end'] = tz_localize_naive(row['end'], tz)
pd.options.mode.chained_assignment = chained_assignment
if row['end'] == section.end:
usable_sections_from_cache.append(row)
for section in sections:
if not section:
continue
try:
rows_matching_start = cached_stat.loc[section.start]
except KeyError:
pass
else:
if isinstance(rows_matching_start, pd.Series):
append_row(rows_matching_start, section)
else:
for row_i in range(rows_matching_start.shape[0]):
row = rows_matching_start.iloc[row_i]
append_row(row, section)
self._data = pd.DataFrame(usable_sections_from_cache)
self._data.sort_index(inplace=True)
def import_from_cache(self, cached_stat, sections):
"""
Converts the data from the cache back into the original
data form used during runtime.
Parameters
----------
cached_stat : DataFrame of cached data
sections : list of nilmtk.TimeFrame objects
describing the sections we want to load stats for.
"""
if cached_stat.empty:
return
tz = get_tz(cached_stat)
usable_sections_from_cache = []
def append_row(row, section):
row = row.astype(object)
# We stripped off the timezone when exporting to cache
# so now we must put the timezone back.
row['end'] = tz_localize_naive(row['end'], tz)
if row['end'] == section.end:
usable_sections_from_cache.append(row)
for section in sections:
if not section:
continue
try:
rows_matching_start = cached_stat.loc[section.start]
except KeyError:
pass
else:
if isinstance(rows_matching_start, pd.Series):
append_row(rows_matching_start, section)
else:
for row_i in range(rows_matching_start.shape[0]):
row = rows_matching_start.iloc[row_i]
append_row(row, section)
self._data = pd.DataFrame(usable_sections_from_cache)
self._data.sort_index(inplace=True)
def import_from_cache(self, cached_stat, sections):
"""
Parameters
----------
cached_stat : DataFrame of cached data
sections : list of nilmtk.TimeFrame objects
describing the sections we want to load stats for.
"""
if cached_stat.empty:
return
tz = get_tz(cached_stat)
usable_sections_from_cache = []
def append_row(row, section):
row = row.astype(object)
# We stripped off the timezone when exporting to cache
# so now we must put the timezone back.
row['end'] = tz_localize_naive(row['end'], tz)
if row['end'] == section.end:
usable_sections_from_cache.append(row)
for section in sections:
if not section:
continue
try:
rows_matching_start = cached_stat.loc[section.start]
except KeyError:
pass
else:
if isinstance(rows_matching_start, pd.Series):
append_row(rows_matching_start, section)
else:
for row_i in range(rows_matching_start.shape[0]):
row = rows_matching_start.iloc[row_i]
append_row(row, section)
self._data = pd.DataFrame(usable_sections_from_cache)
self._data.sort_index(inplace=True)
def import_from_cache(self, cached_stat, sections):
'''
As explained in 'export_to_cache' the sections have to be stored
rowwise. This function parses the lines and rearranges them as a
proper AboveFreqSectionsResult again.
'''
# we (deliberately) use duplicate indices to cache AboveFreqSectionResults
grouped_by_index = cached_stat.groupby(level=0)
tz = get_tz(cached_stat)
for tf_start, df_grouped_by_index in grouped_by_index:
grouped_by_end = df_grouped_by_index.groupby('end')
for tf_end, sections_df in grouped_by_end:
end = tz_localize_naive(tf_end, tz)
timeframe = TimeFrame(tf_start, end)
if timeframe in sections:
timeframes = []
for _, row in sections_df.iterrows():
section_start = tz_localize_naive(
row['section_start'], tz)
section_end = tz_localize_naive(row['section_end'], tz)
timeframes.append(TimeFrame(section_start,
section_end))
self.append(timeframe, {'sections': [timeframes]})
