def _write_monthly_result_sheet(eparam, operating_conditions, wb, results, **kwargs):
"""
:type eparam: pyticas_tetres.ttypes.EstimationRequestInfo
:type operating_conditions: list[pyticas_tetres.rengine.filter.ftypes.ExtFilterGroup]
:type wb:xlsxwriter.Workbook
:type results: list[(list[dict], list[[int, int])]
:type dates: list[datetime.date]
"""
missing_value = kwargs.get('missing_value', MISSING_VALUE)
fields, col_names, sheet_names = report_helper.get_indice_names_for_byindices()
months = util.months(eparam.start_date, eparam.end_date)
for sidx, sheet_name in enumerate(sheet_names):
ws = wb.add_worksheet('monthly %s' % sheet_name)
field_name = fields[sidx]
list_by_oc, months_list, oc_names = _extract_indices(results, operating_conditions, field_name, missing_value)
ws.write_row(0, 0, ['Month'] + oc_names)
ws.write_column(1, 0, ['%04d-%02d' % (month[0], month[1]) for month in months])
col = 1
for oidx, list_for_a_oc in enumerate(list_by_oc):
data = []
for (y, m) in months:
tidx = -1
for midx, (y2, m2) in enumerate(months_list[oidx]):
if y == y2 and m == m2:
tidx = midx
break
if tidx >= 0:
data.append(list_for_a_oc[tidx] if list_for_a_oc[tidx] else missing_value)
else:
data.append(missing_value)
ws.write_column(1, col, data)
col += 1
def _prepare_yearly_monthly_daily_data(eparam, operating_conditions):
"""
:type eparam: pyticas_tetres.ttypes.EstimationRequestInfo
:type operating_conditions: list[pyticas_tetres.rengine.filter.ftypes.ExtFilterGroup]
"""
sdate, edate = util.get_date(eparam.start_date), util.get_date(eparam.end_date)
all_years = util.years(sdate, edate)
all_months = util.months(sdate, edate)
all_dates = util.dates(sdate, edate, weekdays=eparam.weekdays.get_weekdays(), except_holiday=eparam.except_holiday)
all_times = [ dt.time() for (h, m, dt) in _time_of_day_generator(eparam) ]
for oc in operating_conditions:
oc.all_years = all_years
oc.all_months = all_months
oc.all_dates = all_dates
oc.all_times = all_times
if not oc.whole_data:
continue
prev_day = util.get_datetime(oc.whole_data[0].tti.time).date()
years, results, one_year_data, one_month_data, one_day_data = [], [], [], [], []
for ext_data in oc.whole_data:
cur_day = util.get_datetime(ext_data.tti.time).date()
if cur_day.year != prev_day.year:
oc.yearly_data.append((prev_day.year, one_year_data))
one_year_data = []
if cur_day.month != prev_day.month:
oc.monthly_data.append(([prev_day.year, prev_day.month], one_month_data))
one_month_data = []
if cur_day != prev_day:
oc.daily_data.append((prev_day, one_day_data))
one_day_data = []
prev_day = cur_day
one_year_data.append(ext_data)
one_month_data.append(ext_data)
one_day_data.append(ext_data)
if one_year_data:
oc.yearly_data.append((prev_day.year, one_year_data))
if one_month_data:
oc.monthly_data.append(([prev_day.year, prev_day.month], one_month_data))
if one_day_data:
oc.daily_data.append((prev_day, one_day_data))
def _write_monthly_index_variations_by_oc(eparam, operating_conditions,
results_monthly, output_dir,
**kwargs):
"""
:type eparam: pyticas_tetres.ttypes.EstimationRequestInfo
:type operating_conditions: list[pyticas_tetres.rengine.filter.ftypes.ExtFilterGroup]
:type results_monthly: list[(list[dict], list[str])]
:type output_dir: str
"""
monthly_output_dir = os.path.join(output_dir, OUTPUT_DIR_NAME,
'monthly-variations')
if not os.path.exists(monthly_output_dir):
os.makedirs(monthly_output_dir)
marker, color = kwargs.get('marker', 'o'), kwargs.get('color', 'b')
chart_type = kwargs.get('chart_type', 'bar')
all_months = util.months(eparam.start_date, eparam.end_date)
xaxis = np.array(list(range(len(all_months))))
_xaxis, _xlabels = _get_xaxis(
['%04d-%02d' % (m[0], m[1]) for m in all_months])
_xaxis = np.array(_xaxis)
sw = 0.5
for gidx, oc in enumerate(operating_conditions):
results, months = results_monthly[gidx]
if not results:
continue
tts, ttrs, ttis, bi95s, bi85s, pt95s, pt85s = _get_indices(
results, months, all_months, 0)
indices = [ttis, bi95s, bi85s, pt95s, pt85s]
default_maxs = [
DEFAULT_TTI_MAX, DEFAULT_BI_MAX, DEFAULT_BI_MAX, DEFAULT_PTI_MAX,
DEFAULT_PTI_MAX
]
names = [
'Travel Time Index', 'Buffer Index (95th-%ile)',
'Buffer Index (85th-%ile)', 'Planning Time Index (95th-%ile)',
'Planning Time Index (85th-%ile)'
]
for idx, (indice, indice_name) in enumerate(zip(indices, names)):
if not indice:
continue
fig = plt.figure(figsize=(16, 8), facecolor='white')
ax1 = plt.subplot(111)
if chart_type == 'bar':
ax1.bar(xaxis, indice, color=color, label=indice_name)
plt.xticks(_xaxis + 0.5, _xlabels, rotation=70)
else:
ax1.plot(xaxis,
indice,
marker=marker,
c=color,
label=indice_name)
plt.xticks(_xaxis, _xlabels, rotation=70)
plt.grid()
plt.xlim(xmin=0)
ax1.legend(prop={'size': 12})
plt.title('Monthly %s Variations [%s]' % (indice_name, oc.label))
plt.xlabel('Month')
ax1.set_ylim(ymin=0,
ymax=max(default_maxs[idx],
np.nanmax([bi95s, pt95s, ttis])))
ax1.set_ylabel('Index Value')
output_file = _output_path(
monthly_output_dir,
'Monthly %s Variations (%s).png' % (indice_name, oc.label))
plt.tight_layout()
plt.savefig(output_file)
plt.clf()
plt.close(fig)
del indice
gc.collect()
def _write_monthly_index_variations_bar(eparam, operating_conditions,
results_monthly, output_dir):
"""
:type eparam: pyticas_tetres.ttypes.EstimationRequestInfo
:type operating_conditions: list[pyticas_tetres.rengine.filter.ftypes.ExtFilterGroup]
:type results_monthly: list[(list[dict], list[str])]
:type output_dir: str
"""
monthly_output_dir = os.path.join(output_dir, OUTPUT_DIR_NAME,
'monthly-variations')
if not os.path.exists(monthly_output_dir):
os.makedirs(monthly_output_dir)
all_months = util.months(eparam.start_date, eparam.end_date)
xaxis = np.array(list(range(len(all_months))))
_xaxis, _xlabels = _get_xaxis(
['%04d-%02d' % (m[0], m[1]) for m in all_months])
_xaxis = np.array(_xaxis)
for gidx, oc in enumerate(operating_conditions):
results, months = results_monthly[gidx]
if not results:
continue
ch = ColorHatches()
tts, ttrs, ttis, bi95s, bi85s, pt95s, pt85s = _get_indices(
results, months, all_months, 0)
fig = plt.figure(figsize=(16, 8), facecolor='white')
ax1 = plt.subplot(111)
space = 0.4
n = 3
w = (1 - space) / n
sw = w * n / 2
h, c = ch.next()
ax1.bar(xaxis,
bi95s,
width=w,
color=c,
hatch=h,
label='Buffer Index(95th-%ile)')
h, c = ch.next()
ax1.bar(xaxis + 1 * w,
pt95s,
width=w,
color=c,
hatch=h,
label='Planning Time Index(95th-%ile)')
h, c = ch.next()
ax1.bar(xaxis + 2 * w,
ttis,
width=w,
color=c,
hatch=h,
label='Travel Time Index')
plt.xticks(_xaxis + sw, _xlabels, rotation=70)
plt.grid()
plt.xlim(xmin=0)
ax1.legend(prop={'size': 12})
plt.title('Monthly Variations of BI, PTI and TTI [%s]' % oc.label)
plt.xlabel('Month')
ax1.set_ylim(ymin=0,
ymax=max(DEFAULT_MAX_INDEX,
np.nanmax([bi95s, pt95s, ttis])))
ax1.set_ylabel('Index Value')
output_file = _output_path(
monthly_output_dir,
'Monthly-Variations of BI, PTI and TTI (%s).png' % oc.label)
plt.tight_layout()
plt.savefig(output_file)
plt.clf()
plt.close(fig)
del tts, ttrs, ttis, bi95s, bi85s, pt95s, pt85s
gc.collect()