def accumulated_distances(results, route, **kwargs):
"""
:type results: list[RNodeData]
:type route: pyticas.ttypes.Route
:rtype: list[float]
"""
return moe_helper.accumulated_distances(results, route, **kwargs)
def sheet_writer(wb, sheet, results, r, n_data, rows_before_data,
rows_after_data, **kwargs):
"""
:type wb: Workbook
:type sheet: xlsxwriter.workbook.Worksheet
:type results: list[pyticas.ttypes.RNodeData]
:type r: pyticas.ttypes.Route
:type n_data: int
:type rows_before_data: int
:type rows_after_data: int
"""
n_nodes = len(results)
tcm_row = rows_before_data + n_data + rows_after_data
tcmp_row = tcm_row + 1
sheet.write_string(tcm_row, 0, 'TCMH')
sheet.write_string(tcmp_row, 0, 'TCMH(%)')
start_col = 1
for ridx, rnd in enumerate(results):
# last rnode must be excepted because TCMP can exceed 100% if the last station is included
if ridx == n_nodes - 1:
break
congested = [
v for tidx, v in enumerate(rnd.data)
if v and not isinstance(v, str) and v > 0
]
tcm = sum(congested)
tcmp = len(congested) * 100 / n_data
sheet.write_number(tcm_row, ridx + start_col, tcm)
sheet.write_number(tcmp_row, ridx + start_col, tcmp)
tcm_col = n_nodes + 1
tcmp_col = tcm_col + 1
sheet.write_string(0, tcm_col, 'TCMH')
sheet.write_string(0, tcmp_col, 'TCMH(%)')
acc_distances = moe_helper.accumulated_distances(results, r)
for tidx in range(n_data):
# accumulative distances can be same when distance between two stations is less than 0.1 mile
congested = [
acc_distances[ridx] for ridx, rnd in enumerate(results)
if rnd.data[tidx] and not isinstance(rnd.data[tidx], str)
and rnd.data[tidx] > 0
]
congested = list(set(congested))
n_congested = len(congested)
tcm = n_congested * 0.1
tcmp = n_congested * 100.0 / (n_nodes - 1)
sheet.write_number(rows_before_data + tidx, tcm_col, tcm)
sheet.write_number(rows_before_data + tidx, tcmp_col, tcmp)
def _add_sheet(wb, results, _route, **kwargs):
"""
:type wb: Workbook
:type results: list[RNodeData]
:type _route: pyticas.ttypes.Route
:type kwargs: dict
"""
missing_data = kwargs.get('missing_data', cfg.MISSING_VALUE)
prd = results[0].prd
timeline = prd.get_timeline()
n_data = len(timeline)
sheet = wb.add_worksheet(prd.get_date_string())
# print rnode names
sheet.write_row(row=0, col=1, data=[rnd.get_title(**kwargs) for rnd in results])
data_start_row = 1
rows_before_data = 1
rows_after_data = 0
# print used lane information
if kwargs.get('print_lanes', True):
rows_before_data += 1
wrap = wb.add_format({'text_wrap': True})
wrap.set_align('top')
sheet.write_string(data_start_row, 0, 'Lane:Detector', wrap)
sheet.write_row(row=data_start_row, col=1, data=moe_helper.used_lanes(results, _route), cell_format=wrap)
data_start_row += 1
# print accumulated distance from upstream
if kwargs.get('print_distance', True):
rows_before_data += 1
sheet.write_string(data_start_row, 0, 'Distance')
sheet.write_row(row=data_start_row, col=1, data=moe_helper.accumulated_distances(results, _route))
data_start_row += 1
# print confidence level (%)
if kwargs.get('print_confidence', True):
rows_before_data += 1
sheet.write_string(data_start_row, 0, 'Confidence')
sheet.write_row(row=data_start_row, col=1, data=moe_helper.confidences(results))
data_start_row += 1
# print timeline
sheet.write_column(row=data_start_row, col=0, data=timeline)
# print data
for ridx, rnd in enumerate(results):
sheet.write_column(row=data_start_row, col=ridx + 1, data=rnd.data)
data_start_row = len(results[0].data) + data_start_row
# print average
if kwargs.get('print_average', False):
rows_after_data += 1
sheet.write_string(data_start_row, 0, 'Average')
for ridx, rnd in enumerate(results):
rnode_data = [v for v in rnd.data if v and not isinstance(v, str) and v > 0]
if rnode_data:
avg = sum(rnode_data) / len(rnode_data)
else:
avg = missing_data
sheet.write_number(data_start_row, ridx + 1, avg)
data_start_row += 1
# call additional sheet_writer
sheet_writer = kwargs.get('sheet_writer', None)
if sheet_writer:
assert callable(sheet_writer)
sheet_writer(wb, sheet, results, _route, n_data, rows_before_data, rows_after_data, **kwargs)
sheets = wb.worksheets()
if len(sheets) >= 2:
sheets[1].activate()